Gene expression markers for breast cancer prognosis

ABSTRACT

The present invention provides gene sets the expression of which is important in the diagnosis and/or prognosis of breast cancer.

This application is a continuation application of U.S. application Ser.No. 13/473,526, filed May 16, 2012, which is a divisional application ofU.S. application Ser. No. 13/221,549, filed Aug. 30, 2011, now U.S. Pat.No. 8,206,919, which is a divisional application of U.S. applicationSer. No. 12/478,632, filed Jun. 4, 2009, now U.S. Pat. No. 8,034,565,which is a continuation application of U.S. application Ser. No.10/758,307, filed Jan. 14, 2004, now U.S. Pat. No. 7,569,345, whichclaims priority under 35 U.S.C. § 119(e) to U.S. Provisional ApplicationSer. No. 60/440,861, filed Jan. 15, 2003, the entire disclosures ofwhich are hereby expressly incorporated herein by reference.

This application contains a Sequence Listing submitted in ASCII textfile titled “GHDX008DIV2SEQLISTING.TXT,” created May 16, 2012,comprising 82 KB, which is hereby expressly incorporated herein byreference.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention provides genes and gene sets the expression ofwhich is important in the diagnosis and/or prognosis of breast cancer.

Description of the Related Art

Oncologists have a number of treatment options available to them,including different combinations of chemotherapeutic drugs that arecharacterized as “standard of care,” and a number of drugs that do notcarry a label claim for particular cancer, but for which there isevidence of efficacy in that cancer. Best likelihood of good treatmentoutcome requires that patients be assigned to optimal available cancertreatment, and that this assignment be made as quickly as possiblefollowing diagnosis.

Currently, diagnostic tests used in clinical practice are singleanalyte, and therefore do not capture the potential value of knowingrelationships between dozens of different markers. Moreover, diagnostictests are frequently not quantitative, relying on immunohistochemistry.This method often yields different results in different laboratories, inpart because the reagents are not standardized, and in part because theinterpretations are subjective and cannot be easily quantified.RNA-based tests have not often been used because of the problem of RNAdegradation over time and the fact that it is difficult to obtain freshtissue samples from patients for analysis. Fixed paraffin-embeddedtissue is more readily available and methods have been established todetect RNA in fixed tissue. However, these methods typically do notallow for the study of large numbers of genes (DNA or RNA) from smallamounts of material. Thus, traditionally fixed tissue has been rarelyused other than for immunohistochemistry detection of proteins.

Recently, several groups have published studies concerning theclassification of various cancer types by microarray gene expressionanalysis (see, e.g. Golub et al., Science 286:531-537 (1999);Bhattacharjae et al., Proc. Natl. Acad. Sci. USA 98:13790-13795 (2001);Chen-Hsiang et al., Bioinformatics 17 (Suppl. 1):S316-S322 (2001);Ramaswamy et al., Proc. Natl. Acad. Sci. USA 98:15149-15154 (2001)).Certain classifications of human breast cancers based on gene expressionpatterns have also been reported (Martin et al., Cancer Res.60:2232-2238 (2000); West et al., Proc. Natl. Acad. Sci. USA98:11462-11467 (2001); Sorlie et al., Proc. Natl. Acad. Sci. USA98:10869-10874 (2001); Yan et al., Cancer Res. 61:8375-8380 (2001)).However, these studies mostly focus on improving and refining thealready established classification of various types of cancer, includingbreast cancer, and generally do not provide new insights into therelationships of the differentially expressed genes, and do not link thefindings to treatment strategies in order to improve the clinicaloutcome of cancer therapy.

Although modern molecular biology and biochemistry have revealedhundreds of genes whose activities influence the behavior of tumorcells, state of their differentiation, and their sensitivity orresistance to certain therapeutic drugs, with a few exceptions, thestatus of these genes has not been exploited for the purpose ofroutinely making clinical decisions about drug treatments. One notableexception is the use of estrogen receptor (ER) protein expression inbreast carcinomas to select patients to treatment with anti-estrogendrugs, such as tamoxifen. Another exceptional example is the use ofErbB2 (Her2) protein expression in breast carcinomas to select patientswith the Her2 antagonist drug Herceptin® (Genentech, Inc., South SanFrancisco, Calif.).

Despite recent advances, the challenge of cancer treatment remains totarget specific treatment regimens to pathogenically distinct tumortypes, and ultimately personalize tumor treatment in order to maximizeoutcome. Hence, a need exists for tests that simultaneously providepredictive information about patient responses to the variety oftreatment options. This is particularly true for breast cancer, thebiology of which is poorly understood. It is clear that theclassification of breast cancer into a few subgroups, such as ErbB2⁺subgroup, and subgroups characterized by low to absent gene expressionof the estrogen receptor (ER) and a few additional transcriptionalfactors (Perou et al., Nature 406:747-752 (2000)) does not reflect thecellular and molecular heterogeneity of breast cancer, and does notallow the design of treatment strategies maximizing patient response.

SUMMARY OF THE INVENTION

The present invention provides a set of genes, the expression of whichhas prognostic value, specifically with respect to disease-freesurvival.

The present invention accommodates the use of archived paraffin-embeddedbiopsy material for assay of all markers in the set, and therefore iscompatible with the most widely available type of biopsy material. It isalso compatible with several different methods of tumor tissue harvest,for example, via core biopsy or fine needle aspiration. Further, foreach member of the gene set, the invention specifies oligonucleotidesequences that can be used in the test.

In one aspect, the invention concerns a method of predicting thelikelihood of long-term survival of a breast cancer patient without therecurrence of breast cancer, comprising determining the expression levelof one or more prognostic RNA transcripts or their expression productsin a breast cancer tissue sample obtained from the patient, normalizedagainst the expression level of all RNA transcripts or their products inthe breast cancer tissue sample, or of a reference set of RNAtranscripts or their expression products, wherein the prognostic RNAtranscript is the transcript of one or more genes selected from thegroup consisting of: TP53BP2, GRB7, PR, CD68, Bcl2, KRT14, IRS1, CTSL,EstR1, Chk1, IGFBP2, BAG1, CEGP1, STK15, GSTM1, FHIT, RIZ1, AIB1, SURV,BBC3, IGF1R, p27, GATA3, ZNF217, EGFR, CD9, MYBL2, HIF1α, pS2, ErbB3,TOP2B, MDM2, RAD51C, KRT19, TS, Her2, KLK10, β-Catenin, γ-Catenin, MCM2,PI3KC2A, IGF1, TBP, CCNB1, FBXO5, and DR5,

wherein expression of one or more of GRB7, CD68, CTSL, Chk1, AIB1,CCNB1, MCM2, FBXO5, Her2, STK15, SURV, EGFR, MYBL2, HIF1α, and TSindicates a decreased likelihood of long-term survival without breastcancer recurrence, and

the expression of one or more of TP53BP2, PR, Bcl2, KRT14, EstR1,IGFBP2, BAG1, CEGP1, KLK10, β-Catenin, γ-Catenin, DR5, PI3KCA2, RAD51C,GSTM1, FHIT, RIZ1, BBC3, TBP, p27, IRS1, IGF1R, GATA3, ZNF217, CD9, pS2,ErbB3, TOP2B, MDM2, IGF1, and KRT19 indicates an increased likelihood oflong-term survival without breast cancer recurrence.

In a particular embodiment, the expression levels of at least two, or atleast 5, or at least 10, or at least 15 of the prognostic RNAtranscripts or their expression products are determined. In anotherembodiment, the method comprises the determination of the expressionlevels of all prognostic RNA transcripts or their expression products.

In another particular embodiment, the breast cancer is invasive breastcarcinoma.

In a further embodiment, RNA is isolated from a fixed, wax-embeddedbreast cancer tissue specimen of the patient. Isolation may be performedby any technique known in the art, for example from core biopsy tissueor fine needle aspirate cells.

In another aspect, the invention concerns an array comprisingpolynucleotides hybridizing to two or more of the following genes:α-Catenin, AIB1, AKT1, AKT2, β-actin, BAG1, BBC3, Bcl2, CCNB1, CCND1,CD68, CD9, CDH1, CEGP1, Chk1, CIAP1, cMet.2, Contig 27882, CTSL, DR5,EGFR, EIF4E, EPHX1, ErbB3, EstR1, FBXO5, FHIT1 FRP1, GAPDH, GATA3,G-Catenin, GRB7, GRO1, GSTM1, GUS, HER2, HIF1A, HNF3A, IGF1R, IGFBP2,KLK10, KRT14, KRT17, KRT18, KRT19, KRT5, Maspin, MCM2, MCM3, MDM2, MMP9,MTA1, MYBL2, P14ARF, p27, P53, PI3KC2A, PR, PRAME, pS2, RAD51C, 3RB1,RIZ1, STK15, STMY3, SURV, TGFA, TOP2B, TP53BP2, TRAIL, TS, upa, VDR,VEGF, and ZNF217.

In particular embodiments, the array comprises polynucleotideshybridizing to at least 3, or at least 5, or at least 10, or at least15, or at least 20, or all of the genes listed above.

In another specific embodiment, the array comprises polynucleotideshybridizing to the following genes: TP53BP2, GRB7, PR, CD68, Bcl2,KRT14, IRS1, CTSL, EstR1, Chk1, IGFBP2, BAG1, CEGP1, STK15, GSTM1, FHIT,RIZ1, AIB1, SURV, BBC3, IGF1R, p27, GATA3, ZNF217, EGFR, CD9, MYBL2,HIF1α, pS2, RIZ1, ErbB3, TOP2B, MDM2, RAD51C, KRT19, TS, Her2, KLK10,β-Catenin, γ-Catenin, MCM2, PI3KC2A, IGF1, TBP, CCNB1, FBXO5 and DR5.

The polynucleotides can be cDNAs, or oligonucleotides, and the solidsurface on which they are displayed may, for example, be glass.

In another aspect, the invention concerns a method of predicting thelikelihood of long-term survival of a patient diagnosed with invasivebreast cancer, without the recurrence of breast cancer, comprising thesteps of:

(1) determining the expression levels of the RNA transcripts or theexpression products of genes or a gene set selected from the groupconsisting of

-   (a) TP53BP2, Bcl2, BAD, EPHX1, PDGFRβ, DIABLO, XIAP, YB1, CA9, and    KRT8;-   (b) GRB7, CD68, TOP2A, Bcl2, DIABLO, CD3, ID1, PPM1D, MCM6, and    WISP1;-   (c) PR, TP53BP2, PRAME, DIABLO, CTSL, IGFBP2, TIMP1, CA9, MMP9, and    COX2;-   (d) CD68, GRB7, TOP2A, Bcl2, DIABLO, CD3, ID1, PPM1D, MCM6, and    WISP1;-   (e) Bcl2, TP53BP2, BAD, EPHX1, PDGFRβ, DIABLO, XIAP, YB1, CA9, and    KRT8;-   (f) KRT14, KRT5, PRAME, TP53BP2, GUS1, AIB1, MCM3, CCNE1, MCM6, and    ID1;-   (g) PRAME, TP53BP2, EstR1, DIABLO, CTSL, PPM1D, GRB7, DAPK1, BBC3,    and VEGFB;-   (h) CTSL2, GRB7, TOP2A, CCNB1, Bcl2, DIABLO, PRAME, EMS1, CA9, and    EpCAM;-   (i) EstR1, TP53BP2, PRAME, DIABLO, CTSL, PPM1D, GRB7, DAPK1, BBC3,    and VEGFB;-   (k) Chk1, PRAME, TP53BP2, GRB7, CA9, CTSL, CCNB1, TOP2A, tumor size,    and IGFBP2;-   (l) IGFBP2, GRB7, PRAME, DIABLO, CTSL, β-Catenin, PPM1D, Chk1,    WISP1, and LOT1;-   (m) HER2, TP53BP2, Bcl2, DIABLO, TIMP1, EPHX1, TOP2A, TRAIL, CA9,    and AREG;-   (n) BAG1, TP53BP2, PRAME, IL6, CCNB1, PAI1, AREG, tumor size, CA9,    and Ki67;-   (o) CEGP1, TP53BP2, PRAME, DIABLO, Bcl2, COX2, CCNE1, STK15, and    AKT2, and FGF18;-   (p) STK15, TP53BP2, PRAME, IL6, CCNE1, AKT2, DIABLO, cMet, CCNE2,    and COX2;-   (q) KLK10, EstR1, TP53BP2, PRAME, DIABLO, CTSL, PPM1D, GRB7, DAPK1,    and BBC3;-   (r) AIB1, TP53BP2, Bcl2, DIABLO, TIMP1, CD3, p53, CA9, GRB7, and    EPHX1-   (s) BBC3, GRB7, CD68, PRAME, TOP2A, CCNB1, EPHX1, CTSL GSTM1, and    APC;-   (t) CD9, GRB7, CD68, TOP2A, Bcl2, CCNB1, CD3, DIABLO, ID1, and    PPM1D;-   (w) EGFR, KRT14, GRB7, TOP2A, CCNB1, CTSL, Bcl2, TP, KLK10, and CA9;-   (x) HIF1a, PR, DIABLO, PRAME, Chk1, AKT2, GRB7, CCNE1, TOP2A, and    CCNB1;-   (y) MDM2, TP53BP2, DIABLO, Bcl2, AIB1, TIMP1, CD3, p53, CA9, and    HER2;-   (z) MYBL2, TP53BP2, PRAME, IL6, Bcl2, DIABLO, CCNE1, EPHX1, TIMP1,    and CA9;-   (aa) p27, TP53BP2, PRAME, DIABLO, Bcl2, COX2, CCNE1, STK15, AKT2,    and ID1-   (ab) RAD51, GRB7, CD68, TOP2A, CIAP2, CCNB1, BAG1, IL6, FGFR1, and    TP53BP2;-   (ac) SURV, GRB7, TOP2A, PRAME, CTSL, GSTM1, CCNB1, VDR, CA9; and    CCNE2;-   (ad) TOP2B, TP53BP2, DIABLO, Bcl2, TIMP1, AIB1, CA9, p53, KRT8, and    BAD;-   (ae) ZNF217, GRB7, TP53BP2, PRAME, DIABLO, Bcl2, COX2, CCNE1, APC4,    and β-Catenin,    in a breast cancer tissue sample obtained from the patient,    normalized against the expression levels of all RNA transcripts or    their expression products in said breast cancer tissue sample, or of    a reference set of RNA transcripts or their products;

(2) subjecting the data obtained in step (1) to statistical analysis;and

(3) determining whether the likelihood of said long-term survival hasincreased or decreased.

In a further aspect, the invention concerns a method of predicting thelikelihood of long-term survival of a patient diagnosed with estrogenreceptor (ER)-positive invasive breast cancer, without the recurrence ofbreast cancer, comprising the steps of:

(1) determining the expression levels of the RNA transcripts or theexpression products of genes of a gene set selected from the groupconsisting of CD68; CTSL; FBXO5; SURV; CCNB1; MCM2; Chk1; MYBL2; HIF1A;cMET; EGFR; TS; STK15, IGFR1; BC12; HNF3A; TP53BP2; GATA3; BBC3; RAD51C;BAG1; IGFBP2; PR; CD9; RB1; EPHX1; CEGP1; TRAIL; DR5; p27; p53; MTA;RIZ1; ErbB3; TOP2B; EIF4E, wherein expression of the following genes inER-positive cancer is indicative of a reduced likelihood of survivalwithout cancer recurrence following surgery: CD68; CTSL; FBXO5; SURV;CCNB1; MCM2; Chk1; MYBL2; HIF1A; cMET; EGFR; TS; STK15, and whereinexpression of the following genes is indicative of a better prognosisfor survival without cancer recurrence following surgery: IGFR1; BC12;HNF3A; TP53BP2; GATA3; BBC3; RAD51C; BAG1; IGFBP2; PR; CD9; RB1; EPHX1;CEGP1; TRAIL; DR5; p27; p53; MTA; RIZ1; ErbB3; TOP2B; EIF4E.

(2) subjecting the data obtained in step (1) to statistical analysis;and

(3) determining whether the likelihood of said long-term survival hasincreased or decreased.

In yet another aspect, the invention concerns a method of predicting thelikelihood of long-term survival of a patient diagnosed with estrogenreceptor (ER)-negative invasive breast cancer, without the recurrence ofbreast cancer, comprising determining the expression levels of the RNAtranscripts or the expression products of genes of the gene set CCND1;UPA; HNF3A; CDH1; Her2; GRB7; AKT1; STMY3; α-Catenin; VDR; GRO1; KT14;KLK10; Maspin, TGFα, and FRP1, wherein expression of the following genesis indicative of a reduced likelihood of survival without cancerrecurrence: CCND1; UPA; HNF3A; CDH1; Her2; GRB7; AKT1; STMY3; α-Catenin;VDR; GRO1, and wherein expression of the following genes is indicativeof a better prognosis for survival without cancer recurrence: KT14;KLK10; Maspin, TGFα, and FRP1.

In a different aspect, the invention concerns a method of preparing apersonalized genomics profile for a patient, comprising the steps of:

(a) subjecting RNA extracted from a breast tissue obtained from thepatient to gene expression analysis;

(b) determining the expression level of one or more genes selected fromthe breast cancer gene set listed in any one of Tables 1-5, wherein theexpression level is normalized against a control gene or genes andoptionally is compared to the amount found in a breast cancer referencetissue set; and

(c) creating a report summarizing the data obtained by the geneexpression analysis.

The report may, for example, include prediction of the likelihood oflong term survival of the patient and/or recommendation for a treatmentmodality of said patient.

In a further aspect, the invention concerns a method for amplificationof a gene listed in Tables 5A and B by polymerase chain reaction (PCR),comprising performing said PCR by using an amplicon listed in Tables 5Aand B and a primer-probe set listed in Tables 6A-F.

In a still further aspect, the invention concerns a PCR amplicon listedin Tables 5A and B.

In yet another aspect, the invention concerns a PCR primer-probe setlisted in Tables 6A-F.

The invention further concerns a prognostic method comprising:

(a) subjecting a sample comprising breast cancer cells obtained from apatient to quantitative analysis of the expression level of the RNAtranscript of at least one gene selected from the group consisting ofGRB7, CD68, CTSL, Chk1, AIB1, CCNB1, MCM2, FBXO5, Her2, STK15, SURV,EGFR, MYBL2, HIF1α, and TS, or their product, and

(b) identifying the patient as likely to have a decreased likelihood oflong-term survival without breast cancer recurrence if the normalizedexpression levels of the gene or genes, or their products, are elevatedabove a defined expression threshold.

In a different aspect, the invention concerns a prognostic methodcomprising:

(a) subjecting a sample comprising breast cancer cells obtained from apatient to quantitative analysis of the expression level of the RNAtranscript of at least one gene selected from the group consisting ofTP53BP2, PR, Bcl2, KRT14, EstR1, IGFBP2, BAG1, CEGP1, KLK10, β-Catenin,γ-Catenin, DR5, PI3KCA2, RAD51C, GSTM1, FHIT, RIZ1, BBC3, TBP, p27,IRS1, IGF1R, GATA3, ZNF217, CD9, pS2, ErbB3, TOP2B, MDM2, IGF1, andKRT19, and

(b) identifying the patient as likely to have an increased likelihood oflong-term survival without breast cancer recurrence if the normalizedexpression levels of the gene or genes, or their products, are elevatedabove a defined expression threshold.

The invention further concerns a kit comprising one or more of (1)extraction buffer/reagents and protocol; (2) reverse transcriptionbuffer/reagents and protocol; and (3) qPCR buffer/reagents and protocolsuitable for performing any of the foregoing methods.

Table 1 is a list of genes, expression of which correlate with breastcancer survival. Results from a retrospective clinical trial. Binarystatistical analysis.

Table 2 is a list of genes, expression of which correlates with breastcancer survival in estrogen receptor (ER) positive patients. Resultsfrom a retrospective clinical trial. Binary statistical analysis.

Table 3 is a list of genes, expression of which correlates with breastcancer survival in estrogen receptor (ER) negative patients. Resultsfrom a retrospective clinical trial. Binary statistical analysis.

Table 4 is a list of genes, expression of which correlates with breastcancer survival. Results from a retrospective clinical trial. Coxproportional hazards statistical analysis.

Tables 5A and B show a list of genes, expression of which correlate withbreast cancer survival. Results from a retrospective clinical trial. Thetable includes accession numbers for the genes, and amplicon sequencesused for PCR amplification.

Tables 6A-6F The table includes sequences for the forward and reverseprimers (designated by “f” and “r”, respectively) and probes (designatedby “p”) used for PCR amplification of the amplicons listed in Tables5A-B.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT A. Definitions

Unless defined otherwise, technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Singleton et al., Dictionary ofMicrobiology and Molecular Biology 2nd ed., J. Wiley & Sons (New York,N.Y. 1994), and March, Advanced Organic Chemistry Reactions, Mechanismsand Structure 4th ed., John Wiley & Sons (New York, N.Y. 1992), provideone skilled in the art with a general guide to many of the terms used inthe present application.

One skilled in the art will recognize many methods and materials similaror equivalent to those described herein, which could be used in thepractice of the present invention. Indeed, the present invention is inno way limited to the methods and materials described. For purposes ofthe present invention, the following terms are defined below.

The term “microarray” refers to an ordered arrangement of hybridizablearray elements, preferably polynucleotide probes, on a substrate.

The term “polynucleotide,” when used in singular or plural, generallyrefers to any polyribonucleotide or polydeoxribonucleotide, which may beunmodified RNA or DNA or modified RNA or DNA. Thus, for instance,polynucleotides as defined herein include, without limitation, single-and double-stranded DNA, DNA including single- and double-strandedregions, single- and double-stranded RNA, and RNA including single- anddouble-stranded regions, hybrid molecules comprising DNA and RNA thatmay be single-stranded or, more typically, double-stranded or includesingle- and double-stranded regions. In addition, the term“polynucleotide” as used herein refers to triple-stranded regionscomprising RNA or DNA or both RNA and DNA. The strands in such regionsmay be from the same molecule or from different molecules. The regionsmay include all of one or more of the molecules, but more typicallyinvolve only a region of some of the molecules. One of the molecules ofa triple-helical region often is an oligonucleotide. The term“polynucleotide” specifically includes cDNAs. The term includes DNAs(including cDNAs) and RNAs that contain one or more modified bases.Thus, DNAs or RNAs with backbones modified for stability or for otherreasons are “polynucleotides” as that term is intended herein. Moreover,DNAs or RNAs comprising unusual bases, such as inosine, or modifiedbases, such as tritiated bases, are included within the term“polynucleotides” as defined herein. In general, the term“polynucleotide” embraces all chemically, enzymatically and/ormetabolically modified forms of unmodified polynucleotides, as well asthe chemical forms of DNA and RNA characteristic of viruses and cells,including simple and complex cells.

The term “oligonucleotide” refers to a relatively short polynucleotide,including, without limitation, single-stranded deoxyribonucleotides,single- or double-stranded ribonucleotides, RNA:DNA hybrids anddouble-stranded DNAs. Oligonucleotides, such as single-stranded DNAprobe oligonucleotides, are often synthesized by chemical methods, forexample using automated oligonucleotide synthesizers that arecommercially available. However, oligonucleotides can be made by avariety of other methods, including in vitro recombinant DNA-mediatedtechniques and by expression of DNAs in cells and organisms.

The terms “differentially expressed gene,” “differential geneexpression” and their synonyms, which are used interchangeably, refer toa gene whose expression is activated to a higher or lower level in asubject suffering from a disease, specifically cancer, such as breastcancer, relative to its expression in a normal or control subject. Theterms also include genes whose expression is activated to a higher orlower level at different stages of the same disease. It is alsounderstood that a differentially expressed gene may be either activatedor inhibited at the nucleic acid level or protein level, or may besubject to alternative splicing to result in a different polypeptideproduct. Such differences may be evidenced by a change in mRNA levels,surface expression, secretion or other partitioning of a polypeptide,for example. Differential gene expression may include a comparison ofexpression between two or more genes or their gene products, or acomparison of the ratios of the expression between two or more genes ortheir gene products, or even a comparison of two differently processedproducts of the same gene, which differ between normal subjects andsubjects suffering from a disease, specifically cancer, or betweenvarious stages of the same disease. Differential expression includesboth quantitative, as well as qualitative, differences in the temporalor cellular expression pattern in a gene or its expression productsamong, for example, normal and diseased cells, or among cells which haveundergone different disease events or disease stages. For the purpose ofthis invention, “differential gene expression” is considered to bepresent when there is at least an about two-fold, preferably at leastabout four-fold, more preferably at least about six-fold, mostpreferably at least about ten-fold difference between the expression ofa given gene in normal and diseased subjects, or in various stages ofdisease development in a diseased subject.

The phrase “gene amplification” refers to a process by which multiplecopies of a gene or gene fragment are formed in a particular cell orcell line. The duplicated region (a stretch of amplified DNA) is oftenreferred to as “amplicon.” Usually, the amount of the messenger RNA(mRNA) produced, i.e., the level of gene expression, also increases inthe proportion of the number of copies made of the particular geneexpressed.

The term “diagnosis” is used herein to refer to the identification of amolecular or pathological state, disease or condition, such as theidentification of a molecular subtype of head and neck cancer, coloncancer, or other type of cancer.

The term “prognosis” is used herein to refer to the prediction of thelikelihood of cancer-attributable death or progression, includingrecurrence, metastatic spread, and drug resistance, of a neoplasticdisease, such as breast cancer.

The term “prediction” is used herein to refer to the likelihood that apatient will respond either favorably or unfavorably to a drug or set ofdrugs, and also the extent of those responses, or that a patient willsurvive, following surgical removal or the primary tumor and/orchemotherapy for a certain period of time without cancer recurrence. Thepredictive methods of the present invention can be used clinically tomake treatment decisions by choosing the most appropriate treatmentmodalities for any particular patient. The predictive methods of thepresent invention are valuable tools in predicting if a patient islikely to respond favorably to a treatment regimen, such as surgicalintervention, chemotherapy with a given drug or drug combination, and/orradiation therapy, or whether long-term survival of the patient,following surgery and/or termination of chemotherapy or other treatmentmodalities is likely.

The term “long-term” survival is used herein to refer to survival for atleast 3 years, more preferably for at least 8 years, most preferably forat least 10 years following surgery or other treatment.

The term “tumor,” as used herein, refers to all neoplastic cell growthand proliferation, whether malignant or benign, and all pre-cancerousand cancerous cells and tissues.

The terms “cancer” and “cancerous” refer to or describe thephysiological condition in mammals that is typically characterized byunregulated cell growth. Examples of cancer include but are not limitedto, breast cancer, colon cancer, lung cancer, prostate cancer,hepatocellular cancer, gastric cancer, pancreatic cancer, cervicalcancer, ovarian cancer, liver cancer, bladder cancer, cancer of theurinary tract, thyroid cancer, renal cancer, carcinoma, melanoma, andbrain cancer.

The “pathology” of cancer includes all phenomena that compromise thewell-being of the patient. This includes, without limitation, abnormalor uncontrollable cell growth, metastasis, interference with the normalfunctioning of neighboring cells, release of cytokines or othersecretory products at abnormal levels, suppression or aggravation ofinflammatory or immunological response, neoplasia, premalignancy,malignancy, invasion of surrounding or distant tissues or organs, suchas lymph nodes, etc.

“Stringency” of hybridization reactions is readily determinable by oneof ordinary skill in the art, and generally is an empirical calculationdependent upon probe length, washing temperature, and saltconcentration. In general, longer probes require higher temperatures forproper annealing, while shorter probes need lower temperatures.Hybridization generally depends on the ability of denatured DNA toreanneal when complementary strands are present in an environment belowtheir melting temperature. The higher the degree of desired homologybetween the probe and hybridizable sequence, the higher the relativetemperature which can be used. As a result, it follows that higherrelative temperatures would tend to make the reaction conditions morestringent, while lower temperatures less so. For additional details andexplanation of stringency of hybridization reactions, see Ausubel etal., Current Protocols in Molecular Biology, Wiley IntersciencePublishers, (1995).

“Stringent conditions” or “high stringency conditions”, as definedherein, typically: (1) employ low ionic strength and high temperaturefor washing, for example 0.015 M sodium chloride/0.0015 M sodiumcitrate/0.1% sodium dodecyl sulfate at 50° C.; (2) employ duringhybridization a denaturing agent, such as formamide, for example, 50%(v/v) formamide with 0.1% bovine serum albumin/0.1% Ficoll/0.1%polyvinylpyrrolidone/50 mM sodium phosphate buffer at pH 6.5 with 750 mMsodium chloride, 75 mM sodium citrate at 42° C.; or (3) employ 50%formamide, 5×SSC (0.75 M NaCl, 0.075 M sodium citrate), 50 mM sodiumphosphate (pH 6.8), 0.1% sodium pyrophosphate, 5×Denhardt's solution,sonicated salmon sperm DNA (50 μg/ml), 0.1% SDS, and 10% dextran sulfateat 42° C., with washes at 42° C. in 0.2×SSC (sodium chloride/sodiumcitrate) and 50% formamide at 55° C., followed by a high-stringency washconsisting of 0.1×SSC containing EDTA at 55° C.

“Moderately stringent conditions” may be identified as described bySambrook et al., Molecular Cloning: A Laboratory Manual, New York: ColdSpring Harbor Press, 1989, and include the use of washing solution andhybridization conditions (e.g., temperature, ionic strength and % SDS)less stringent that those described above. An example of moderatelystringent conditions is overnight incubation at 37° C. in a solutioncomprising: 20% formamide, 5×SSC (150 mM NaCl, 15 mM trisodium citrate),50 mM sodium phosphate (pH 7.6), 5×Denhardt's solution, 10% dextransulfate, and 20 mg/ml denatured sheared salmon sperm DNA, followed bywashing the filters in 1×SSC at about 37-50° C. The skilled artisan willrecognize how to adjust the temperature, ionic strength, etc. asnecessary to accommodate factors such as probe length and the like.

In the context of the present invention, reference to “at least one,”“at least two,” “at least five,” etc. of the genes listed in anyparticular gene set means any one or any and all combinations of thegenes listed.

The terms “expression threshold,” and “defined expression threshold” areused interchangeably and refer to the level of a gene or gene product inquestion above which the gene or gene product serves as a predictivemarker for patient survival without cancer recurrence. The threshold isdefined experimentally from clinical studies such as those described inthe Example below. The expression threshold can be selected either formaximum sensitivity, or for maximum selectivity, or for minimum error.The determination of the expression threshold for any situation is wellwithin the knowledge of those skilled in the art.

B. Detailed Description

The practice of the present invention will employ, unless otherwiseindicated, conventional techniques of molecular biology (includingrecombinant techniques), microbiology, cell biology, and biochemistry,which are within the skill of the art. Such techniques are explainedfully in the literature, such as, “Molecular Cloning: A LaboratoryManual”, 2^(nd) edition (Sambrook et al., 1989); “OligonucleotideSynthesis” (M. J. Gait, ed., 1984); “Animal Cell Culture” (R. I.Freshney, ed., 1987); “Methods in Enzymology” (Academic Press, Inc.);“Handbook of Experimental Immunology”, 4^(th) edition (D. M. Weir & C.C. Blackwell, eds., Blackwell Science Inc., 1987); “Gene TransferVectors for Mammalian Cells” (J. M. Miller & M. P. Calos, eds., 1987);“Current Protocols in Molecular Biology” (F. M. Ausubel et al., eds.,1987); and “PCR: The Polymerase Chain Reaction”, (Mullis et al., eds.,1994).

1. Gene Expression Profiling

In general, methods of gene expression profiling can be divided into twolarge groups: methods based on hybridization analysis ofpolynucleotides, and methods based on sequencing of polynucleotides. Themost commonly used methods known in the art for the quantification ofmRNA expression in a sample include northern blotting and in situhybridization (Parker & Barnes, Methods in Molecular Biology 106:247-283(1999)); RNAse protection assays (Hod, Biotechniques 13:852-854 (1992));and reverse transcription polymerase chain reaction (RT-PCR) (Weis etal., Trends in Genetics 8:263-264 (1992)). Alternatively, antibodies maybe employed that can recognize specific duplexes, including DNAduplexes, RNA duplexes, and DNA-RNA hybrid duplexes or DNA-proteinduplexes. Representative methods for sequencing-based gene expressionanalysis include Serial Analysis of Gene Expression (SAGE), and geneexpression analysis by massively parallel signature sequencing (MPSS).

2. Reverse Transcriptase PCR (RT-PCR)

Of the techniques listed above, the most sensitive and most flexiblequantitative method is RT-PCR, which can be used to compare mRNA levelsin different sample populations, in normal and tumor tissues, with orwithout drug treatment, to characterize patterns of gene expression, todiscriminate between closely related mRNAs, and to analyze RNAstructure.

The first step is the isolation of mRNA from a target sample. Thestarting material is typically total RNA isolated from human tumors ortumor cell lines, and corresponding normal tissues or cell lines,respectively. Thus RNA can be isolated from a variety of primary tumors,including breast, lung, colon, prostate, brain, liver, kidney, pancreas,spleen, thymus, testis, ovary, uterus, etc., tumor, or tumor cell lines,with pooled DNA from healthy donors. If the source of mRNA is a primarytumor, mRNA can be extracted, for example, from frozen or archivedparaffin-embedded and fixed (e.g. formalin-fixed) tissue samples.

General methods for mRNA extraction are well known in the art and aredisclosed in standard textbooks of molecular biology, including Ausubelet al., Current Protocols of Molecular Biology, John Wiley and Sons(1997). Methods for RNA extraction from paraffin embedded tissues aredisclosed, for example, in Rupp and Locker, Lab Invest. 56:A67 (1987),and De Andrés et al., BioTechniques 18:42044 (1995). In particular, RNAisolation can be performed using purification kit, buffer set andprotease from commercial manufacturers, such as Qiagen, according to themanufacturer's instructions. For example, total RNA from cells inculture can be isolated using Qiagen RNeasy mini-columns. Othercommercially available RNA isolation kits include MasterPure™ CompleteDNA and RNA Purification Kit (EPICENTRE®, Madison, Wis.), and ParaffinBlock RNA Isolation Kit (Ambion, Inc.). Total RNA from tissue samplescan be isolated using RNA Stat-60 (Tel-Test). RNA prepared from tumorcan be isolated, for example, by cesium chloride density gradientcentrifugation.

As RNA cannot serve as a template for PCR, the first step in geneexpression profiling by RT-PCR is the reverse transcription of the RNAtemplate into cDNA, followed by its exponential amplification in a PCRreaction. The two most commonly used reverse transcriptases are avilomyeloblastosis virus reverse transcriptase (AMV-RT) and Moloney murineleukemia virus reverse transcriptase (MMLV-RT). The reversetranscription step is typically primed using specific primers, randomhexamers, or oligo-dT primers, depending on the circumstances and thegoal of expression profiling. For example, extracted RNA can bereverse-transcribed using a GeneAmp RNA PCR kit (Perkin Elmer, CA, USA),following the manufacturer's instructions. The derived cDNA can then beused as a template in the subsequent PCR reaction.

Although the PCR step can use a variety of thermostable DNA-dependentDNA polymerases, it typically employs the Taq DNA polymerase, which hasa 5′-3′ nuclease activity but lacks a 3′-5′ proofreading endonucleaseactivity. Thus, TaqMan® PCR typically utilizes the 5′-nuclease activityof Taq or Tth polymerase to hydrolyze a hybridization probe bound to itstarget amplicon, but any enzyme with equivalent 5′ nuclease activity canbe used. Two oligonucleotide primers are used to generate an amplicontypical of a PCR reaction. A third oligonucleotide, or probe, isdesigned to detect nucleotide sequence located between the two PCRprimers. The probe is non-extendible by Taq DNA polymerase enzyme, andis labeled with a reporter fluorescent dye and a quencher fluorescentdye. Any laser-induced emission from the reporter dye is quenched by thequenching dye when the two dyes are located close together as they areon the probe. During the amplification reaction, the Taq DNA polymeraseenzyme cleaves the probe in a template-dependent manner. The resultantprobe fragments disassociate in solution, and signal from the releasedreporter dye is free from the quenching effect of the secondfluorophore. One molecule of reporter dye is liberated for each newmolecule synthesized, and detection of the unquenched reporter dyeprovides the basis for quantitative interpretation of the data.

TaqMan® RT-PCR can be performed using commercially available equipment,such as, for example, ABI PRISM 7700™ Sequence Detection System™(Perkin-Elmer-Applied Biosystems, Foster City, Calif., USA), orLightcycler (Roche Molecular Biochemicals, Mannheim, Germany). In apreferred embodiment, the 5′ nuclease procedure is run on a real-timequantitative PCR device such as the ABI PRISM 7700™ Sequence DetectionSystem™. The system consists of a thermocycler, laser, charge-coupleddevice (CCD), camera and computer. The system amplifies samples in a96-well format on a thermocycler. During amplification, laser-inducedfluorescent signal is collected in real-time through fiber optics cablesfor all 96 wells, and detected at the CCD. The system includes softwarefor running the instrument and for analyzing the data.

5′-Nuclease assay data are initially expressed as Ct, or the thresholdcycle. As discussed above, fluorescence values are recorded during everycycle and represent the amount of product amplified to that point in theamplification reaction. The point when the fluorescent signal is firstrecorded as statistically significant is the threshold cycle (C_(t)).

To minimize errors and the effect of sample-to-sample variation, RT-PCRis usually performed using an internal standard. The ideal internalstandard is expressed at a constant level among different tissues, andis unaffected by the experimental treatment. RNAs most frequently usedto normalize patterns of gene expression are mRNAs for the housekeepinggenes glyceraldehyde-3-phosphate-dehydrogenase (GAPDH) and β-actin.

A more recent variation of the RT-PCR technique is the real timequantitative PCR, which measures PCR product accumulation through adual-labeled fluorigenic probe (i.e., TaqMan® probe). Real time PCR iscompatible both with quantitative competitive PCR, where internalcompetitor for each target sequence is used for normalization, and withquantitative comparative PCR using a normalization gene contained withinthe sample, or a housekeeping gene for RT-PCR. For further details see,e.g. Held et al., Genome Research 6:986-994 (1996).

The steps of a representative protocol for profiling gene expressionusing fixed, paraffin-embedded tissues as the RNA source, including mRNAisolation, purification, primer extension and amplification are given invarious published journal articles {for example: T. E. Godfrey et al.,J. Molec. Diagnostics 2: 84-91 [2000]; K. Specht et al., Am. J. Pathol.158: 419-29 [2001]}. Briefly, a representative process starts withcutting about 10 μm thick sections of paraffin-embedded tumor tissuesamples. The RNA is then extracted, and protein and DNA are removed.After analysis of the RNA concentration, RNA repair and/or amplificationsteps may be included, if necessary, and RNA is reverse transcribedusing gene specific promoters followed by RT-PCR.

According to one aspect of the present invention, PCR primers and probesare designed based upon intron sequences present in the gene to beamplified. In this embodiment, the first step in the primer/probe designis the delineation of intron sequences within the genes. This can bedone by publicly available software, such as the DNA BLAT softwaredeveloped by Kent, W. J., Genome Res. 12(4):656-64 (2002), or by theBLAST software including its variations. Subsequent steps follow wellestablished methods of PCR primer and probe design.

In order to avoid non-specific signals, it is important to maskrepetitive sequences within the introns when designing the primers andprobes. This can be easily accomplished by using the Repeat Maskerprogram available on-line through the Baylor College of Medicine, whichscreens DNA sequences against a library of repetitive elements andreturns a query sequence in which the repetitive elements are masked.The masked intron sequences can then be used to design primer and probesequences using any commercially or otherwise publicly availableprimer/probe design packages, such as Primer Express (AppliedBiosystems); MGB assay-by-design (Applied Biosystems); Primer3 (SteveRozen and Helen J. Skaletsky (2000) Primer3 on the WWW for general usersand for biologist programmers. In: Krawetz S, Misener S (eds)Bioinformatics Methods and Protocols: Methods in Molecular Biology.Humana Press, Totowa, N.J., pp 365-386)

The most important factors considered in PCR primer design includeprimer length, melting temperature (Tm), and G/C content, specificity,complementary primer sequences, and 3′-end sequence. In general, optimalPCR primers are generally 17-30 bases in length, and contain about20-80%, such as, for example, about 50-60% G+C bases. Tm's between 50and 80° C., e.g. about 50 to 70° C. are typically preferred.

For further guidelines for PCR primer and probe design see, e.g.Dieffenbach, C. W. et al., “General Concepts for PCR Primer Design” in:PCR Primer, A Laboratory Manual, Cold Spring Harbor Laboratory Press,New York, 1995, pp. 133-155; Innis and Gelfand, “Optimization of PCRs”in: PCR Protocols, A Guide to Methods and Applications, CRC Press,London, 1994, pp. 5-11; and Plasterer, T. N. Primerselect: Primer andprobe design. Methods Mol. Biol. 70:520-527 (1997), the entiredisclosures of which are hereby expressly incorporated by reference.

3. Microarrays

Differential gene expression can also be identified, or confirmed usingthe microarray technique. Thus, the expression profile of breastcancer-associated genes can be measured in either fresh orparaffin-embedded tumor tissue, using microarray technology. In thismethod, polynucleotide sequences of interest (including cDNAs andoligonucleotides) are plated, or arrayed, on a microchip substrate. Thearrayed sequences are then hybridized with specific DNA probes fromcells or tissues of interest. Just as in the RT-PCR method, the sourceof mRNA typically is total RNA isolated from human tumors or tumor celllines, and corresponding normal tissues or cell lines. Thus RNA can beisolated from a variety of primary tumors or tumor cell lines. If thesource of mRNA is a primary tumor, mRNA can be extracted, for example,from frozen or archived paraffin-embedded and fixed (e.g.formalin-fixed) tissue samples, which are routinely prepared andpreserved in everyday clinical practice.

In a specific embodiment of the microarray technique, PCR amplifiedinserts of cDNA clones are applied to a substrate in a dense array.Preferably at least 10,000 nucleotide sequences are applied to thesubstrate. The microarrayed genes, immobilized on the microchip at10,000 elements each, are suitable for hybridization under stringentconditions. Fluorescently labeled cDNA probes may be generated throughincorporation of fluorescent nucleotides by reverse transcription of RNAextracted from tissues of interest. Labeled cDNA probes applied to thechip hybridize with specificity to each spot of DNA on the array. Afterstringent washing to remove non-specifically bound probes, the chip isscanned by confocal laser microscopy or by another detection method,such as a CCD camera. Quantitation of hybridization of each arrayedelement allows for assessment of corresponding mRNA abundance. With dualcolor fluorescence, separately labeled cDNA probes generated from twosources of RNA are hybridized pairwise to the array. The relativeabundance of the transcripts from the two sources corresponding to eachspecified gene is thus determined simultaneously. The miniaturized scaleof the hybridization affords a convenient and rapid evaluation of theexpression pattern for large numbers of genes. Such methods have beenshown to have the sensitivity required to detect rare transcripts, whichare expressed at a few copies per cell, and to reproducibly detect atleast approximately two-fold differences in the expression levels(Schena et al., Proc. Natl. Acad. Sci. USA 93(2):106-149 (1996)).Microarray analysis can be performed by commercially availableequipment, following manufacturer's protocols, such as by using theAffymetrix GenChip technology, or Incyte's microarray technology.

The development of microarray methods for large-scale analysis of geneexpression makes it possible to search systematically for molecularmarkers of cancer classification and outcome prediction in a variety oftumor types.

4. Serial Analysis of Gene Expression (SAGE)

Serial analysis of gene expression (SAGE) is a method that allows thesimultaneous and quantitative analysis of a large number of genetranscripts, without the need of providing an individual hybridizationprobe for each transcript. First, a short sequence tag (about 10-14 bp)is generated that contains sufficient information to uniquely identify atranscript, provided that the tag is obtained from a unique positionwithin each transcript. Then, many transcripts are linked together toform long serial molecules, that can be sequenced, revealing theidentity of the multiple tags simultaneously. The expression pattern ofany population of transcripts can be quantitatively evaluated bydetermining the abundance of individual tags, and identifying the genecorresponding to each tag. For more details see, e.g. Velculescu et al.,Science 270:484-487 (1995); and Velculescu et al., Cell 88:243-51(1997).

5. MassARRAY Technology

The MassARRAY (Sequenom, San Diego, Calif.) technology is an automated,high-throughput method of gene expression analysis using massspectrometry (MS) for detection. According to this method, following theisolation of RNA, reverse transcription and PCR amplification, the cDNAsare subjected to primer extension. The cDNA-derived primer extensionproducts are purified, and dipensed on a chip array that is pre-loadedwith the components needed for MALTI-TOF MS sample preparation. Thevarious cDNAs present in the reaction are quantitated by analyzing thepeak areas in the mass spectrum obtained.

6. Gene Expression Analysis by Massively Parallel Signature Sequencing(MPSS)

This method, described by Brenner et al., Nature Biotechnology18:630-634 (2000), is a sequencing approach that combines non-gel-basedsignature sequencing with in vitro cloning of millions of templates onseparate 5 μm diameter microbeads. First, a microbead library of DNAtemplates is constructed by in vitro cloning. This is followed by theassembly of a planar array of the template-containing microbeads in aflow cell at a high density (typically greater than 3×10⁶microbeads/cm²). The free ends of the cloned templates on each microbeadare analyzed simultaneously, using a fluorescence-based signaturesequencing method that does not require DNA fragment separation. Thismethod has been shown to simultaneously and accurately provide, in asingle operation, hundreds of thousands of gene signature sequences froma yeast cDNA library.

7. Immunohistochemistry

Immunohistochemistry methods are also suitable for detecting theexpression levels of the prognostic markers of the present invention.Thus, antibodies or antisera, preferably polyclonal antisera, and mostpreferably monoclonal antibodies specific for each marker are used todetect expression. The antibodies can be detected by direct labeling ofthe antibodies themselves, for example, with radioactive labels,fluorescent labels, hapten labels such as, biotin, or an enzyme such ashorse radish peroxidase or alkaline phosphatase. Alternatively,unlabeled primary antibody is used in conjunction with a labeledsecondary antibody, comprising antisera, polyclonal antisera or amonoclonal antibody specific for the primary antibody.Immunohistochemistry protocols and kits are well known in the art andare commercially available.

8. Proteomics

The term “proteome” is defined as the totality of the proteins presentin a sample (e.g. tissue, organism, or cell culture) at a certain pointof time. Proteomics includes, among other things, study of the globalchanges of protein expression in a sample (also referred to as“expression proteomics”). Proteomics typically includes the followingsteps: (1) separation of individual proteins in a sample by 2-D gelelectrophoresis (2-D PAGE); (2) identification of the individualproteins recovered from the gel, e.g. my mass spectrometry or N-terminalsequencing, and (3) analysis of the data using bioinformatics.Proteomics methods are valuable supplements to other methods of geneexpression profiling, and can be used, alone or in combination withother methods, to detect the products of the prognostic markers of thepresent invention.

9. General Description of the mRNA Isolation, Purification andAmplification

The steps of a representative protocol for profiling gene expressionusing fixed, paraffin-embedded tissues as the RNA source, including mRNAisolation, purification, primer extension and amplification are given invarious published journal articles {for example: T. E. Godfrey et al. J.Molec. Diagnostics 2: 84-91 [2000]; K. specht et al., Am. J. Pathol.158: 419-29 [2001]}. Briefly, a representative process starts withcutting about 10 μm thick sections of paraffin-embedded tumor tissuesamples. The RNA is then extracted, and protein and DNA are removed.After analysis of the RNA concentration, RNA repair and/or amplificationsteps may be included, if necessary, and RNA is reverse transcribedusing gene specific promoters followed by RT-PCR. Finally, the data areanalyzed to identify the best treatment option(s) available to thepatient on the basis of the characteristic gene expression patternidentified in the tumor sample examined.

10. Breast Cancer Gene Set, Assayed Gene Subsequences, and ClinicalApplication of Gene Expression Data

An important aspect of the present invention is to use the measuredexpression of certain genes by breast cancer tissue to provideprognostic information. For this purpose it is necessary to correct for(normalize away) both differences in the amount of RNA assayed andvariability in the quality of the RNA used. Therefore, the assaytypically measures and incorporates the expression of certainnormalizing genes, including well known housekeeping genes, such asGAPDH and Cyp1. Alternatively, normalization can be based on the mean ormedian signal (Ct) of all of the assayed genes or a large subset thereof(global normalization approach). On a gene-by-gene basis, measurednormalized amount of a patient tumor mRNA is compared to the amountfound in a breast cancer tissue reference set. The number (N) of breastcancer tissues in this reference set should be sufficiently high toensure that different reference sets (as a whole) behave essentially thesame way. If this condition is met, the identity of the individualbreast cancer tissues present in a particular set will have nosignificant impact on the relative amounts of the genes assayed.Usually, the breast cancer tissue reference set consists of at leastabout 30, preferably at least about 40 different FPE breast cancertissue specimens. Unless noted otherwise, normalized expression levelsfor each mRNA/tested tumor/patient will be expressed as a percentage ofthe expression level measured in the reference set. More specifically,the reference set of a sufficiently high number (e.g. 40) of tumorsyields a distribution of normalized levels of each mRNA species. Thelevel measured in a particular tumor sample to be analyzed falls at somepercentile within this range, which can be determined by methods wellknown in the art. Below, unless noted otherwise, reference to expressionlevels of a gene assume normalized expression relative to the referenceset although this is not always explicitly stated.

Further details of the invention will be described in the followingnon-limiting Example

EXAMPLE A Phase II Study of Gene Expression in 79 Malignant BreastTumors

A gene expression study was designed and conducted with the primary goalto molecularly characterize gene expression in paraffin-embedded, fixedtissue samples of invasive breast ductal carcinoma, and to explore thecorrelation between such molecular profiles and disease-free survival.

Study Design

Molecular assays were performed on paraffin-embedded, formalin-fixedprimary breast tumor tissues obtained from 79 individual patientsdiagnosed with invasive breast cancer. All patients in the study had 10or more positive nodes. Mean age was 57 years, and mean clinical tumorsize was 4.4 cm. Patients were included in the study only ifhistopathologic assessment, performed as described in the Materials andMethods section, indicated adequate amounts of tumor tissue andhomogeneous pathology.

Materials and Methods

Each representative tumor block was characterized by standardhistopathology for diagnosis, semi-quantitative assessment of amount oftumor, and tumor grade. A total of 6 sections (10 microns in thicknesseach) were prepared and placed in two Costar Brand Microcentrifuge Tubes(Polypropylene, 1.7 mL tubes, clear; 3 sections in each tube). If thetumor constituted less than 30% of the total specimen area, the samplemay have been crudely dissected by the pathologist, using grossmicrodissection, putting the tumor tissue directly into the Costar tube.

If more than one tumor block was obtained as part of the surgicalprocedure, the block most representative of the pathology was used foranalysis.

Gene Expression Analysis

mRNA was extracted and purified from fixed, paraffin-embedded tissuesamples, and prepared for gene expression analysis as described insection 9 above.

Molecular assays of quantitative gene expression were performed byRT-PCR, using the ABI PRISM 7900™ Sequence Detection System™(Perkin-Elmer-Applied Biosystems, Foster City, Calif., USA). ABI PRISM7900™ consists of a thermocycler, laser, charge-coupled device (CCD),camera and computer. The system amplifies samples in a 384-well formaton a thermocycler. During amplification, laser-induced fluorescentsignal is collected in real-time through fiber optics cables for all 384wells, and detected at the CCD. The system includes software for runningthe instrument and for analyzing the data.

Analysis and Results

Tumor tissue was analyzed for 185 cancer-related genes and 7 referencegenes. The threshold cycle (CT) values for each patient were normalizedbased on the median of the 7 reference genes for that particularpatient. Clinical outcome data were available for all patients from areview of registry data and selected patient charts.

Outcomes were classified as:

0 died due to breast cancer or to unknown cause or alive with breastcancer recurrence; 1 alive without breast cancer recurrence or died dueto a cause other than breast cancer

Analysis was performed by:

1. Analysis of the relationship between normalized gene expression andthe binary outcomes of 0 or 1.

2. Analysis of the relationship between normalized gene expression andthe time to outcome (0 or 1 as defined above) where patients who werealive without breast cancer recurrence or who died due to a cause otherthan breast cancer were censored. This approach was used to evaluate theprognostic impact of individual genes and also sets of multiple genes.

Analysis of Patients with Invasive Breast Carcinoma by Binary Approach

In the first (binary) approach, analysis was performed on all 79patients with invasive breast carcinoma. At test was performed on thegroups of patients classified as either no recurrence and no breastcancer related death at three years, versus recurrence, or breastcancer-related death at three years, and the p-values for thedifferences between the groups for each gene were calculated.

Table 1 lists the 47 genes for which the p-value for the differencesbetween the groups was <0.10. The first column of mean expression valuespertains to patients who neither had a metastatic recurrence of nor diedfrom breast cancer. The second column of mean expression values pertainsto patients who either had a metastatic recurrence of or died frombreast cancer.

TABLE 1 Mean Mean t-value df p Valid N Valid N Bcl2 −0.15748 −1.228164.00034 75 0.000147 35 42 PR −2.67225 −5.49747 3.61540 75 0.000541 35 42IGF1R −0.59390 −1.71506 3.49158 75 0.000808 35 42 BAG1 0.18844 −0.685093.42973 75 0.000985 35 42 CD68 −0.52275 0.10983 −3.41186 75 0.001043 3542 EstR1 −0.35581 −3.00699 3.32190 75 0.001384 35 42 CTSL −0.64894−0.09204 −3.26781 75 0.001637 35 42 IGFBP2 −0.81181 −1.78398 3.24158 750.001774 35 42 GATA3 1.80525 0.57428 3.15608 75 0.002303 35 42 TP53BP2−4.71118 −6.09289 3.02888 75 0.003365 35 42 EstR1 3.67801 1.646933.01073 75 0.003550 35 42 CEGP1 −2.02566 −4.25537 2.85620 75 0.005544 3542 SURV −3.67493 −2.96982 −2.70544 75 0.008439 35 42 p27 0.80789 0.288072.55401 75 0.012678 35 42 Chk1 −3.37981 −2.80389 −2.46979 75 0.015793 3542 BBC3 −4.71789 −5.62957 2.46019 75 0.016189 35 42 ZNF217 1.100380.62730 2.42282 75 0.017814 35 42 EGFR −2.88172 −2.20556 −2.34774 750.021527 35 42 CD9 1.29955 0.91025 2.31439 75 0.023386 35 42 MYBL2−3.77489 −3.02193 −2.29042 75 0.024809 35 42 HIF1A −0.44248 0.03740−2.25950 75 0.026757 35 42 GRB7 −1.96063 −1.05007 −2.25801 75 0.02685435 42 pS2 −1.00691 −3.13749 2.24070 75 0.028006 35 42 RIZ1 −7.62149−8.38750 2.20226 75 0.030720 35 42 ErbB3 −6.89508 −7.44326 2.16127 750.033866 35 42 TOP2B 0.45122 0.12665 2.14616 75 0.035095 35 42 MDM21.09049 0.69001 2.10967 75 0.038223 35 42 PRAME −6.40074 −7.704242.08126 75 0.040823 35 42 GUS −1.51683 −1.89280 2.05200 75 0.043661 3542 RAD51C −5.85618 −6.71334 2.04575 75 0.044288 35 42 AIB1 −3.08217−2.28784 −2.00600 75 0.048462 35 42 STK15 −3.11307 −2.59454 −2.00321 750.048768 35 42 GAPDH −0.35829 −0.02292 −1.94326 75 0.055737 35 42 FHIT−3.00431 −3.67175 1.86927 75 0.065489 35 42 KRT19 2.52397 2.016941.85741 75 0.067179 35 42 TS −2.83607 −2.29048 −1.83712 75 0.070153 3542 GSTM1 −3.69140 −4.38623 1.83397 75 0.070625 35 42 G-Catenin 0.31875−0.15524 1.80823 75 0.074580 35 42 AKT2 0.78858 0.46703 1.79276 750.077043 35 42 CCNB1 −4.26197 −3.51628 −1.78803 75 0.077810 35 42PI3KC2A −2.27401 −2.70265 1.76748 75 0.081215 35 42 FBXO5 −4.72107−4.24411 −1.75935 75 0.082596 35 42 DR5 −5.80850 −6.55501 1.74345 750.085353 35 42 CIAP1 −2.81825 −3.09921 1.72480 75 0.088683 35 42 MCM2−2.87541 −2.50683 −1.72061 75 0.089445 35 42 CCND1 1.30995 0.809051.68794 75 0.095578 35 42 EIF4E −5.37657 −6.47156 1.68169 75 0.096788 3542

In the foregoing Table 1, negative t-values indicate higher expression,associated with worse outcomes, and, inversely, higher (positive)t-values indicate higher expression associated with better outcomes.Thus, for example, elevated expression of the CD68 gene (t-value=−3.41,CT mean alive<CT mean deceased) indicates a reduced likelihood ofdisease free survival. Similarly, elevated expression of the BC12 gene(t-value=4.00; CT mean alive>CT mean deceased) indicates an increasedlikelihood of disease free survival.

Based on the data set forth in Table 1, the expression of any of thefollowing genes in breast cancer above a defined expression thresholdindicates a reduced likelihood of survival without cancer recurrencefollowing surgery: Grb7, CD68, CTSL, Chk1, Her2, STK15, AIB1, SURV,EGFR, MYBL2, HIF1α.

Based on the data set forth in Table 1, the expression of any of thefollowing genes in breast cancer above a defined expression thresholdindicates a better prognosis for survival without cancer recurrencefollowing surgery: TP53BP2, PR, Bcl2, KRT14, EstR1, IGFBP2, BAG1, CEGP1,KLK10, β Catenin, GSTM1, FHIT, Riz1, IGF1, BBC3, IGFR1, TBP, p27, IRS1,IGF1R, GATA3, CEGP1, ZNF217, CD9, pS2, ErbB3, TOP2B, MDM2, RAD51, andKRT19.

Analysis of ER Positive Patients by Binary Approach

57 patients with normalized CT for estrogen receptor (ER)>0 (i.e., ERpositive patients) were subjected to separate analysis. At test wasperformed on the two groups of patients classified as either norecurrence and no breast cancer related death at three years, orrecurrence or breast cancer-related death at three years, and thep-values for the differences between the groups for each gene werecalculated. Table 2, below, lists the genes where the p-value for thedifferences between the groups was <0.105. The first column of meanexpression values pertains to patients who neither had a metastaticrecurrence nor died from breast cancer. The second column of meanexpression values pertains to patients who either had a metastaticrecurrence of or died from breast cancer.

TABLE 2 Mean Mean t-value df p Valid N Valid N IGF1R −0.13975 −1.004353.65063 55 0.000584 30 27 Bcl2 0.15345 −0.70480 3.55488 55 0.000786 3027 CD68 −0.54779 0.19427 −3.41818 55 0.001193 30 27 HNF3A 0.39617−0.63802 3.20750 55 0.002233 30 27 CTSL −0.66726 0.00354 −3.20692 550.002237 30 27 TP53BP2 −4.81858 −6.44425 3.13698 55 0.002741 30 27 GATA32.33386 1.40803 3.02958 55 0.003727 30 27 BBC3 −4.54979 −5.72333 2.9194355 0.005074 30 27 RAD51C −5.63363 −6.94841 2.85475 55 0.006063 30 27BAG1 0.31087 −0.50669 2.61524 55 0.011485 30 27 IGFBP2 −0.49300 −1.309832.59121 55 0.012222 30 27 FBXO5 −4.86333 −4.05564 −2.56325 55 0.01313530 27 EstR1 0.68368 −0.66555 2.56090 55 0.013214 30 27 PR −1.89094−3.86602 2.52803 55 0.014372 30 27 SURV −3.87857 −3.10970 −2.49622 550.015579 30 27 CD9 1.41691 0.91725 2.43043 55 0.018370 30 27 RB1−2.51662 −2.97419 2.41221 55 0.019219 30 27 EPHX1 −3.91703 −5.850972.29491 55 0.025578 30 27 CEGP1 −1.18600 −2.95139 2.26608 55 0.027403 3027 CCNB1 −4.44522 −3.35763 −2.25148 55 0.028370 30 27 TRAIL 0.34893−0.56574 2.20372 55 0.031749 30 27 EstR1 4.60346 3.60340 2.20223 550.031860 30 27 DR5 −5.71827 −6.79088 2.14548 55 0.036345 30 27 MCM2−2.96800 −2.48458 −2.10518 55 0.039857 30 27 Chk1 −3.46968 −2.85708−2.08597 55 0.041633 30 27 p27 0.94714 0.49656 2.04313 55 0.045843 30 27MYBL2 −3.97810 −3.14837 −2.02921 55 0.047288 30 27 GUS −1.42486 −1.829001.99758 55 0.050718 30 27 P53 −1.08810 −1.47193 1.92087 55 0.059938 3027 HIF1A −0.40925 0.11688 −1.91278 55 0.060989 30 27 cMet −6.36835−5.58479 −1.88318 55 0.064969 30 27 EGFR −2.95785 −2.28105 −1.86840 550.067036 30 27 MTA1 −7.55365 −8.13656 1.81479 55 0.075011 30 27 RIZ1−7.52785 −8.25903 1.79518 55 0.078119 30 27 ErbB3 −6.62488 −7.108261.79255 55 0.078545 30 27 TOP2B 0.54974 0.27531 1.74888 55 0.085891 3027 EIF4E −5.06603 −6.31426 1.68030 55 0.098571 30 27 TS −2.95042−2.36167 −1.67324 55 0.099959 30 27 STK15 −3.25010 −2.72118 −1.64822 550.105010 30 27

For each gene, a classification algorithm was utilized to identify thebest threshold value (CT) for using each gene alone in predictingclinical outcome.

Based on the data set forth in Table 2, expression of the followinggenes in ER-positive cancer above a defined expression level isindicative of a reduced likelihood of survival without cancer recurrencefollowing surgery: CD68; CTSL; FBXO5; SURV; CCNB1; MCM2; Chk1; MYBL2;HIF1A; cMET; EGFR; TS; STK15. Many of these genes (CD68, CTSL, SURV,CCNB1, MCM2, Chk1, MYBL2, EGFR, and STK15) were also identified asindicators of poor prognosis in the previous analysis, not limited toER-positive breast cancer. Based on the data set forth in Table 2,expression of the following genes in ER-positive cancer above a definedexpression level is indicative of a better prognosis for survivalwithout cancer recurrence following surgery: IGFR1; BC12; HNF3A;TP53BP2; GATA3; BBC3; RAD51C; BAG1; IGFBP2; PR; CD9; RB1; EPHX1; CEGP1;TRAIL; DR5; p27; p53; MTA; RIZ1; ErbB3; TOP2B; EIF4E. Of the lattergenes, IGFR1; BC12; TP53BP2; GATA3; BBC3; RAD51C; BAG1; IGFBP2; PR; CD9;CEGP1; DR5; p27; RIZ1; ErbB3; TOP2B; EIF4E have also been identified asindicators of good prognosis in the previous analysis, not limited toER-positive breast cancer.

Analysis of ER Negative Patients by Binary Approach

Twenty patients with normalized CT for estrogen receptor (ER)<1.6 (i.e.,ER negative patients) were subjected to separate analysis. At test wasperformed on the two groups of patients classified as either norecurrence and no breast cancer related death at three years, orrecurrence or breast cancer-related death at three years, and thep-values for the differences between the groups for each gene werecalculated. Table 3 lists the genes where the p-value for thedifferences between the groups was <0.118. The first column of meanexpression values pertains to patients who neither had a metastaticrecurrence nor died from breast cancer. The second column of meanexpression values pertains to patients who either had a metastaticrecurrence of or died from breast cancer.

TABLE 3 Mean Mean t-value df p Valid N Valid N KRT14 −1.95323 −6.692314.03303 18 0.000780 5 15 KLK10 −2.68043 −7.11288 3.10321 18 0.006136 515 CCND1 −1.02285 0.03732 −2.77992 18 0.012357 5 15 Upa −0.91272−0.04773 −2.49460 18 0.022560 5 15 HNF3A −6.04780 −2.36469 −2.43148 180.025707 5 15 Maspin −3.56145 −6.18678 2.40169 18 0.027332 5 15 CDH1−3.54450 −2.34984 −2.38755 18 0.028136 5 15 HER2 −1.48973 1.53108−2.35826 18 0.029873 5 15 GRB7 −2.55289 0.00036 −2.32890 18 0.031714 515 AKT1 −0.36849 0.46222 −2.29737 18 0.033807 5 15 TGFA −4.03137−5.67225 2.28546 18 0.034632 5 15 FRP1 1.45776 −1.39459 2.27884 180.035097 5 15 STMY3 −1.59610 −0.26305 −2.23191 18 0.038570 5 15 Contig27882 −4.27585 −7.34338 2.18700 18 0.042187 5 15 A-Catenin −1.19790−0.39085 −2.15624 18 0.044840 5 15 VDR −4.37823 −2.37167 −2.15620 180.044844 5 15 GRO1 −3.65034 −5.97002 2.12286 18 0.047893 5 15 MCM3−3.86041 −5.55078 2.10030 18 0.050061 5 15 B-actin 4.69672 5.19190−2.04951 18 0.055273 5 15 HIF1A −0.64183 −0.10566 −2.02301 18 0.058183 515 MMP9 −8.90613 −7.35163 −1.88747 18 0.075329 5 15 VEGF 0.37904 1.10778−1.87451 18 0.077183 5 15 PRAME −4.95855 −7.41973 1.86668 18 0.078322 515 AIB1 −3.12245 −1.92934 −1.86324 18 0.078829 5 15 KRT5 −1.32418−3.62027 1.85919 18 0.079428 5 15 KRT18 1.08383 2.25369 −1.83831 180.082577 5 15 KRT17 −0.69073 −3.56536 1.78449 18 0.091209 5 15 P14ARF−1.87104 −3.36534 1.63923 18 0.118525 5 15

Based on the data set forth in Table 3, expression of the followinggenes in ER-negative cancer above a defined expression level isindicative of a reduced likelihood of survival without cancer recurrence(p<0.05): CCND1; UPA; HNF3A; CDH1; Her2; GRB7; AKT1; STMY3; α-Catenin;VDR; GRO1. Only 2 of these genes (Her2 and Grb7) were also identified asindicators of poor prognosis in the previous analysis, not limited toER-negative breast cancer. Based on the data set forth in Table 3,expression of the following genes in ER-negative cancer above a definedexpression level is indicative of a better prognosis for survivalwithout cancer recurrence (KT14; KLK10; Maspin, TGFα, and FRP1. Of thelatter genes, only KLK10 has been identified as an indicator of goodprognosis in the previous analysis, not limited to ER-negative breastcancer.

Analysis of Multiple Genes and Indicators of Outcome

Two approaches were taken in order to determine whether using multiplegenes would provide better discrimination between outcomes.

First, a discrimination analysis was performed using a forward stepwiseapproach. Models were generated that classified outcome with greaterdiscrimination than was obtained with any single gene alone.

According to a second approach (time-to-event approach), for each gene aCox Proportional Hazards model (see, e.g. Cox, D. R., and Oakes, D.(1984), Analysis of Survival Data, Chapman and Hall, London, New York)was defined with time to recurrence or death as the dependent variable,and the expression level of the gene as the independent variable. Thegenes that have a p-value<0.10 in the Cox model were identified. Foreach gene, the Cox model provides the relative risk (RR) of recurrenceor death for a unit change in the expression of the gene. One can chooseto partition the patients into subgroups at any threshold value of themeasured expression (on the CT scale), where all patients withexpression values above the threshold have higher risk, and all patientswith expression values below the threshold have lower risk, or viceversa, depending on whether the gene is an indicator of bad (RR>1.01) orgood (RR<1.01) prognosis. Thus, any threshold value will definesubgroups of patients with respectively increased or decreased risk. Theresults are summarized in Table 4. The third column, with the heading:exp(coef), shows RR values.

TABLE 4 Gene coef exp(coef) se(coef) z p TP53BP2 −0.21892 0.8033860.068279 −3.20625 0.00134 GRB7 0.235697 1.265791 0.073541 3.2049920.00135 PR −0.10258 0.90251 0.035864 −2.86018 0.00423 CD68 0.4656231.593006 0.167785 2.775115 0.00552 Bcl2 −0.26769 0.765146 0.100785−2.65603 0.00791 KRT14 −0.11892 0.887877 0.046938 −2.53359 0.0113 PRAME−0.13707 0.871912 0.054904 −2.49649 0.0125 CTSL 0.431499 1.5395640.185237 2.329444 0.0198 EstR1 −0.07686 0.926018 0.034848 −2.205610.0274 Chk1 0.284466 1.329053 0.130823 2.174441 0.0297 IGFBP2 −0.21520.806376 0.099324 −2.16669 0.0303 HER2 0.155303 1.168011 0.0726332.13818 0.0325 BAG1 −0.22695 0.796959 0.106377 −2.13346 0.0329 CEGP1−0.07879 0.924236 0.036959 −2.13177 0.033 STK15 0.27947 1.3224280.132762 2.105039 0.0353 KLK10 −0.11028 0.895588 0.05245 −2.10248 0.0355B. Catenin −0.16536 0.847586 0.084796 −1.95013 0.0512 EstR1 −0.08030.922842 0.042212 −1.90226 0.0571 GSTM1 −0.13209 0.876266 0.072211−1.82915 0.0674 TOP2A −0.11148 0.894512 0.061855 −1.80222 0.0715 AIB10.152968 1.165288 0.086332 1.771861 0.0764 FHIT −0.15572 0.8558020.088205 −1.7654 0.0775 RIZ1 −0.17467 0.839736 0.099464 −1.75609 0.0791SURV 0.185784 1.204162 0.106625 1.742399 0.0814 IGF1 −0.10499 0.9003380.060482 −1.73581 0.0826 BBC3 −0.1344 0.874243 0.077613 −1.73163 0.0833IGF1R −0.13484 0.873858 0.077889 −1.73115 0.0834 DIABLO 0.284336 1.328880.166556 1.707148 0.0878 TBP −0.34404 0.7089 0.20564 −1.67303 0.0943 p27−0.26002 0.771033 0.1564 −1.66256 0.0964 IRS1 −0.07585 0.926957 0.046096−1.64542 0.0999

The binary and time-to-event analyses, with few exceptions, identifiedthe same genes as prognostic markers. For example, comparison of Tables1 and 4 shows that 10 genes were represented in the top 15 genes in bothlists. Furthermore, when both analyses identified the same gene at[p<0.10], which happened for 21 genes, they were always concordant withrespect to the direction (positive or negative sign) of the correlationwith survival/recurrence. Overall, these results strengthen theconclusion that the identified markers have significant prognosticvalue.

For Cox models comprising more than two genes (multivariate models),stepwise entry of each individual gene into the model is performed,where the first gene entered is pre-selected from among those geneshaving significant univariate p-values, and the gene selected for entryinto the model at each subsequent step is the gene that best improvesthe fit of the model to the data. This analysis can be performed withany total number of genes. In the analysis the results of which areshown below, stepwise entry was performed for up to 10 genes.

Multivariate analysis is performed using the following equation:RR=exp[coef(geneA)×Ct(geneA)+coef(geneB)×Ct(geneB)+coef(geneC)×Ct(geneC)+. . . ].

In this equation, coefficients for genes that are predictors ofbeneficial outcome are positive numbers and coefficients for genes thatare predictors of unfavorable outcome are negative numbers. The “Ct”values in the equation are ΔCts, i.e. reflect the difference between theaverage normalized Ct value for a population and the normalized Ctmeasured for the patient in question. The convention used in the presentanalysis has been that ΔCts below and above the population average havepositive signs and negative signs, respectively (reflecting greater orlesser mRNA abundance). The relative risk (RR) calculated by solvingthis equation will indicate if the patient has an enhanced or reducedchance of long-term survival without cancer recurrence.

Multivariate Gene Analysis of 79 Patients with Invasive Breast Carcinoma

A multivariate stepwise analysis, using the Cox Proportional HazardsModel, was performed on the gene expression data obtained for all 79patients with invasive breast carcinoma. The following ten-gene setshave been identified by this analysis as having particularly strongpredictive value of patient survival:

-   (a) TP53BP2, Bcl2, BAD, EPHX1, PDGFRβ, DIABLO, XIAP, YB1, CA9, and    KRT8.-   (b) GRB7, CD68, TOP2A, Bcl2, DIABLO, CD3, ID1, PPM1D, MCM6, and    WISP1.-   (c) PR, TP53BP2, PRAME, DIABLO, CTSL, IGFBP2, TIMP1, CA9, MMP9, and    COX2.-   (d) CD68, GRB7, TOP2A, Bcl2, DIABLO, CD3, ID1, PPM1D, MCM6, and    WISP1.-   (e) Bcl2, TP53BP2, BAD, EPHX1, PDGFRβ, DIABLO, XIAP, YB1, CA9, and    KRT8.-   (f) KRT14, KRT5, PRAME, TP53BP2, GUS1, AIB1, MCM3, CCNE1, MCM6, and    ID1.-   (g) PRAME, TP53BP2, EstR1, DIABLO, CTSL, PPM1D, GRB7, DAPK1, BBC3,    and VEGFB.-   (h) CTSL2, GRB7, TOP2A, CCNB1, Bcl2, DIABLO, PRAME, EMS1, CA9, and    EpCAM.-   (i) EstR1, TP53BP2, PRAME, DIABLO, CTSL, PPM1D, GRB7, DAPK1, BBC3,    and VEGFB.-   (k) Chk1, PRAME, p53BP2, GRB7, CA9, CTSL, CCNB1, TOP2A, tumor size,    and IGFBP2.-   (l) IGFBP2, GRB7, PRAME, DIABLO, CTSL, β-Catenin, PPM1D, Chk1,    WISP1, and LOT1.-   (m) HER2, TP53BP2, Bcl2, DIABLO, TIMP1, EPHX1, TOP2A, TRAIL, CA9,    and AREG.-   (n) BAG1, TP53BP2, PRAME, IL6, CCNB1, PAI1, AREG, tumor size, CA9,    and Ki67.-   (o) CEGP1, TP53BP2, PRAME, DIABLO, Bcl2, COX2, CCNE1, STK15, and    AKT2, and FGF18.-   (p) STK15, TP53BP2, PRAME, IL6, CCNE1, AKT2, DIABLO, cMet, CCNE2,    and COX2.-   (q) KLK10, EstR1, TP53BP2, PRAME, DIABLO, CTSL, PPM1D, GRB7, DAPK1,    and BBC3.-   (r) AIB1, TP53BP2, Bcl2, DIABLO, TIMP1, CD3, p53, CA9, GRB7, and    EPHX1-   (s) BBC3, GRB7, CD68, PRAME, TOP2A, CCNB1, EPHX1, CTSL GSTM1, and    APC.-   (t) CD9, GRB7, CD68, TOP2A, Bcl2, CCNB1, CD3, DIABLO, ID1, and    PPM1D.-   (w) EGFR, KRT14, GRB7, TOP2A, CCNB1, CTSL, Bcl2, TP, KLK10, and CA9.-   (x) HIF1a, PR, DIABLO, PRAME, Chk1, AKT2, GRB7, CCNE1, TOP2A, and    CCNB1.-   (y) MDM2, TP53BP2, DIABLO, Bcl2, AIB1, TIMP1, CD3, p53, CA9, and    HER2.-   (z) MYBL2, TP53BP2, PRAME, IL6, Bcl2, DIABLO, CCNE1, EPHX1, TIMP1,    and CA9.-   (aa) p27, TP53BP2, PRAME, DIABLO, Bcl2, COX2, CCNE1, STK15, AKT2,    and ID1.-   (ab) RAD51, GRB7, CD68, TOP2A, CIAP2, CCNB1, BAG1, IL6, FGFR1, and    TP53BP2.-   (ac) SURV, GRB7, TOP2A, PRAME, CTSL, GSTM1, CCNB1, VDR, CA9, and    CCNE2.-   (ad) TOP2B, TP53BP2, DIABLO, Bcl2, TIMP1, AIB1, CA9, p53, KRT8, and    BAD.-   (ae) ZNF217, GRB7, p53BP2, PRAME, DIABLO, Bcl2, COX2, CCNE1, APC4,    and β-Catenin.

While the present invention has been described with reference to whatare considered to be the specific embodiments, it is to be understoodthat the invention is not limited to such embodiments. To the contrary,the invention is intended to cover various modifications and equivalentsincluded within the spirit and scope of the appended claims. Forexample, while the disclosure focuses on the identification of variousbreast cancer associated genes and gene sets, and on the personalizedprognosis of breast cancer, similar genes, gene sets and methodsconcerning other types of cancer are specifically within the scopeherein.

All references cited throughout the disclosure are hereby expresslyincorporated by reference.

TABLE 5A SEQ ID Gene Accession Seq NO: AIB1 NM_006534GCGGCGAGTTTCCGATTTAAAGCTGAGCTGCGAGGAAAATGGCGGCGGGAGGATCAAAATACTTGCTGGATGGTGGACTCA1 AKT1 NM_005163CGCTTCTATGGCGCTGAGATTGTGTCAGCCCTGGACTACCTGCACTCGGAGAAGAACGTGGTGTACCGGGA2 AKT2 NM_001626TCCTGCCACCCTTCAAACCTCAGGTCACGTCCGAGGTCGACACAAGGTACTTCGATGATGAATTTACCGCC3 APC NM_000038GGACAGCAGGAATGTGTTTCTCCATACAGGTCACGGGGAGCCAATGGTTCAGAAACAAATCGAGTGGGT 4AREG NM_001657TGTGAGTGAAATGCCTTCTAGTAGTGAACCGTCCTCGGGAGCCGACTATGACTACTCAGAAGAGTATGATAACGAACCACAA5 B- NM_001101CAGCAGATGTGGATCAGCAAGCAGGAGTATGACGAGTCCGGCCCCTCCATCGTCCACCGCAAATGC 6actin B- NM_001904GGCTCTTGTGCGTACTGTCCTTCGGGCTGGTGACAGGGAAGACATCACTGAGCCTGCCATCTGTGCTCTTCGTCATCTGA7 Cate- nin BAD NM_032989GGGTCAGGTGCCTCGAGATCGGGCTTGGGCCCAGAGCATGTTCCAGATCCCAGAGTTTGAGCCGAGTGAGCAG8 BAG1 NM_004323CGTTGTCAGCACTTGGAATACAAGATGGTTGCCGGGTCATGTTAATTGGGAAAAAGAACAGTCCACAGGAAGAGGTTGAAC9 BBC3 NM_014417CCTGGAGGGTCCTGTACAATCTCATCATGGGACTCCTGCCCTTACCCAGGGGCCACAGAGCCCCCGAGATGGAGCCCAATTAG10 Bcl2 NM_000633CAGATGGACCTAGTACCCACTGAGATTTCCACGCCGAAGGACAGCGATGGGAAAAATGCCCTTAAATCATAGG11 CA9 NM_001216ATCCTAGCCCTGGTTTTTGGCCTCCTTTTTGCTGTCACCAGCGTCGCGTTCCTTGTGCAGATGAGAAGGCAG12 CCNB1 NM_031966TTCAGGTTGTTGCAGGAGACCATGTACATGACTGTCTCCATTATTGATCGGTTCATGCAGAATAATTGTGTGCCCAAGAAGAT13 G CCND1 NM_001758GCATGTTCGTGGCCTCTAAGATGAAGGAGACCATCCCCCTGACGGCCGAGAAGCTGTGCATCTACACCG 14CCNE1 NM_001238AAAGAAGATGATGACCGGGTTTACCCAAACTCAACGTGCAAGCCTCGGATTATTGCACCATCCAGAGGCTC15 CCNE2 NM_057749ATGCTGTGGCTCCTTCCTAACTGGGGCTTTCTTGACATGTAGGTTGCTTGGTAATAACCTTTTTGTATATCACAATTTGGGT16 CD3z NM_000734AGATGAAGTGGAAGGCGCTTTTCACCGCGGCCATCCTGCAGGCACAGTTGCCGATTACAGAGGCA 17CD68 NM_001251TGGTTCCCAGCCCTGTGTCCACCTCCAAGCCCAGATTCAGATTCGAGTCATGTACACAACCCAGGGTGGAGGAG18 CD9 NM_001769GGGCGTGGAACAGTTTATCTCAGACATCTGCCCCAAGAAGGACGTACTCGAAACCTTCACCGTG 19 CDH1NM_004360TGAGTGTCCCCCGGTATCTTCCCCGCCCTGCCAATCCCGATGAAATTGGAAATTTTATTGATGAAAATCTGAAAGCGGCTG20 CEGP1 NM_020974TGACAATCAGCACACCTGCATTCACCGCTCGGAAGAGGGCCTGAGCTGCATGAATAAGGATCACGGCTGTAGTCACA21 Chk1 NM_001274GATAAATTGGTACAAGGGATCAGCTTTTCCCAGCCCACATGTCCTGATCATATGCTTTTGAATAGTCAGTTACTTGGCACCC22 CIAP1 NM_001166TGCCTGTGGTGGGAAGCTCAGTAACTGGGAACCAAAGGATGATGCTATGTCAGAACACCGGAGGCATTTTCC23 cIAP2 NM_001165GGATATTTCCGTGGCTCTTATTCAAACTCTCCATCAAATCCTGTAAACTCCAGAGCAAATCAAGATTTTTCTGCCTTGATGAG24 AAG cMet NM_000245GACATTTCCAGTCCTGCAGTCAATGCCTCTCTGCCCCACCCTTTGTTCAGTGTGGCTGGTGCCACGACAAATGTGTGCGATCG25 GAG Contig AK000618GGCATCCTGGCCCAAAGTTTCCCAAATCCAGGCGGCTAGAGGCCCACTGCTTCCCAACTACCAGCTGAGGGGGTC26 27882 COX2 NM_000963TCTGCAGAGTTGGAAGCACTCTATGGTGACATCGATGCTGTGGAGCTGTATCCTGCCCTTCTGGTAGAAAAGCCTCGGC27 CTSL NM_001912GGGAGGCTTATCTCACTGAGTGAGCAGAATCTGGTAGACTGCTCTGGGCCTCAAGGCAATGAAGGCTGCAATGG28 CTSL2 NM_001333TGTCTCACTGAGCGAGCAGAATCTGGTGGACTGTTCGCGTCCTCAAGGCAATCAGGGCTGCAATGGT 29DAPK1 NM_004938CGCTGACATCATGAATGTTCCTCGACCGGCTGGAGGCGAGTTTGGATATGACAAAGACACATCGTTGCTGAAAGAGA30 DIABLO NM_019887CACAATGGCGGCTCTGAAGAGTTGGCTGTCGCGCAGCGTAACTTCATTCTTCAGGTACAGACAGTGTTTGTGT31

TABLE 5B SEQ ID Gene Accession Seq NO: DR5 NM_003842CTCTGAGACAGTGCTTCGATGACTTTGCAGACTTGGTGCCCTTTGACTCCTGGGAGCCGCTCATGAGGAAGTTGGGCCTCAT32 GG EGFR NM_005228TGTCGATGGACTTCCAGAACCACCTGGGCAGCTGCCAAAAGTGTGATCCAAGCTGTCCCAAT 33 EIF4ENM_001968GATCTAAGATGGCGACTGTCGAACCGGAAACCACCCCTACTCCTAATCCCCCGACTACAGAAGAGGAGAAAACGGAATCTAA34 EMS1 NM_005231GGCAGTGTCACTGAGTCCTTGAAATCCTCCCCTGCCCCGCGGGTCTCTGGATTGGGACGCACAGTGCA 35EpCAM NM_002354GGGCCCTCCAGAACAATGATGGGCTTTATGATCCTGACTGCGATGAGAGCGGGCTCTTTAAGGCCAAGCAGTGCA36 EPHX1 NM_000120ACCGTAGGCTCTGCTCTGAATGACTCTCCTGTGGGTCTGGCTGCCTATATTCTAGAGAAGTTTTCCACCTGGACCA37 ErbB3 NM_001982CGGTTATGTCATGCCAGATACACACCTCAAAGGTACTCCCTCCTCCCGGGAAGGCACCCTTTCTTCAGTGGGTCTCAGTTC38 EstR1 NM_000125CGTGGTGCCCCTCTATGACCTGCTGCTGGAGATGCTGGACGCCCACCGCCTACATGCGCCCACTAGCC 39FBXO5 NM_012177GGCTATTCCTCATTTTCTCTACAAAGTGGCCTCAGTGAACATGAAGAAGGTAGCCTCCTGGAGGAGAATTTCGGTGACAGTC40 TACAATCC FGF18 NM_003862CGGTAGTCAAGTCCGGATCAAGGGCAAGGAGACGGAATTCTACCTGTGCATGAACCGCAAAGGCAAGC 41FGFR1 NM_023109CACGGGACATTCACCACATCGACTACTATAAAAAGACAACCAACGGCCGACTGCCTGTGAAGTGGATGGCACCC42 FHIT NM_002012CCAGTGGAGCGCTTCCATGACCTGCGTCCTGATGAAGTGGCCGATTTGTTTCAGACGACCCAGAGAG 43FRP1 NM_003012TTGGTACCTGTGGGTTAGCATCAAGTTCTCCCCAGGGTAGAATTCAATCAGAGCTCCAGTTTGCATTTGGATGTG44 G- NM_002230TCAGCAGCAAGGGCATCATGGAGGAGGATGAGGCCTGCGGGCGCCAGTACACGCTCAAGAAAACCACC 45Cate- nin GAPDH NM_002046ATTCCACCCATGGCAAATTCCATGGCACCGTCAAGGCTGAGAACGGGAAGCTTGTCATCAATGGAAATCCCATC46 GATA3 NM_002051CAAAGGAGCTCACTGTGGTGTCTGTGTTCCAACCACTGAATCTGGACCCCATCTGTGAATAAGCCATTCTGACTC47 GRB7 NM_005310CCATCTGCATCCATCTTGTTTGGGCTCCCCACCCTTGAGAAGTGCCTCAGATAATACCCTGGTGGCC 48GRO1 NM_001511CGAAAAGATGCTGAACAGTGACAAATCCAACTGACCAGAAGGGAGGAGGAAGCTCACTGGTGGCTGTTCCTGA49 GSTM1 NM_000561AAGCTATGAGGAAAAGAAGTACACGATGGGGGACGCTCCTGATTATGACAGAAGCCAGTGGCTGAATGAAAAATTCAAGCTG50 GGCC GUS NM_000181CCCACTCAGTAGCCAAGTCACAATGTTTGGAAAACAGCCCGTTTACTTGAGCAAGACTGATACCACCTGCGTG51 HER2 NM_004448CGGTGTGAGAAGTGCAGCAAGCCCTGTGCCCGAGTGTGCTATGGTCTGGGCATGGAGCACTTGCGAGAGG52 HIF1A NM_001530TGAACATAAAGTCTGCAACATGGAAGGTATTGCACTGCACAGGCCACATTCACGTATATGATACCAACAGTAACCAACCTCA53 HNF3A NM_004496TCCAGGATGTTAGGAACTGTGAAGATGGAAGGGCATGAAACCAGCGACTGGAACAGCTACTACGCAGACACGC54 ID1 NM_002165AGAACCGCAAGGTGAGCAAGGTGGAGATTCTCCAGCACGTCATCGACTACATCAGGGACCTTCAGTTGGA55 IGF1 NM_000618TCCGGAGCTGTGATCTAAGGAGGCTGGAGATGTATTGCGCACCCCTCAAGCCTGCCAAGTCAGCTCGCTCTGTCCG56 IGF1R NM_000875GCATGGTAGCCGAAGATTTCACAGTCAAAATCGGAGATTTTGGTATGACGCGAGATATCTATGAGACAGACTATTACCGGAA57 A IGFBP2 NM_000597GTGGACAGCACCATGAACATGTTGGGCGGGGGAGGCAGTGCTGGCCGGAAGCCCCTCAAGTCGGGTATGAAGG58 IL6 NM_000600CCTGAACCTTCCAAAGATGGCTGAAAAAGATGGATGCTTCCAATCTGGATTCAATGAGGAGACTTGCCTGGT59 IRS1 NM_005544CCACAGCTCACCTTCTGTCAGGTGTCCATCCCAGCTCCAGCCAGCTCCCAGAGAGGAAGAGACTGGCACTGAGG60 IG-67 NM_002417CGGACTTTGGGTGCGACTTGACGAGCGGTGGTTCGACAAGTGGCCTTGCGGGCCGGATCGTCCCAGTGGAAGAGTTGTAA61 KLK10 NM_002776GCCCAGAGGCTCCATCGTCCATCCTCTTCCTCCCCAGTCGGCTGAACTCTCCCCTTGTCTGCACTGTTCAAACCTCTG62

TABLE 5C SEQ ID Gene Accession Seq NO: KRT14 NM_000526GGCCTGCTGAGATCAAAGACTACAGTCCCTACTTCAAGACCATTGAGGACCTGAGGAACAAGATTCTCACAGCCACAGT63 GGAC KRT17 NM_000422CGAGGATTGGTTCTTCAGCAAGACAGAGGAACTGAACCGCGAGGTGGCCACCAACAGTGAGCTGGTGCAGAGT64 KRT18 NM_000224AGAGATCGAGGCTCTCAAGGAGGAGCTGCTCTTCATGAAGAAGAACCACGAAGAGGAAGTAAAAGGCC 65KRT19 NM_002276TGAGCGGCAGAATCAGGAGTACCAGCGGCTCATGGACATCAAGTCGCGGCTGGAGCAGGAGATTGCCACCTACCGCA66 KRT5 NM_000424TCAGTGGAGAAGGAGTTGGACCAGTCAACATCTCTGTTGTCACAAGCAGTGTTTCCTCTGGATATGGCA 67KRT8 NM_002273GGATGAAGCTTACATGAACAAGGTAGAGCTGGAGTCTCGCCTGGAAGGGCTGACCGACGAGATCAACTTCCTCAGGCAG68 CTATATG LOT1 NM_002656GGAAAGACCACCTGAAAAACCACCTCCAGACCCACGACCCCAACAAAATGGCCTTTGGGTGTGAGGAGTGTGGGAAGAA69 variant 1 GTAC Maspin NM_002639CAGATGGCCACTTTGAGAACATTTTAGCTGACAACAGTGTGAACGACCAGACCAAAATCCTTGTGGTTAATGCTGCC70 MCM2 NM_004526GACTTTTGCCCGCTACCTTTCATTCCGGCGTGACAACAATGAGCTGTTGCTCTTCATACTGAAGCAGTTAGTGGC71 MCM3 NM_002388GGAGAACAATCCCCTTGAGACAGAATATGGCCTTTCTGTCTACAAGGATCACCAGACCATCACCATCCAGGAGAT72 MCM6 NM_005915TGATGGTCCTATGTGTCACATTCATCACAGGTTTCATACCAACACAGGCTTCAGCACTTCCTTTGGTGTGTTTCCTGTC73 CCA MDM2 NM_002392CTACAGGGACGCCATCGAATCCGGATCTTGATGCTGGTGTAAGTGAACATTCAGGTGATTGGTTGGAT 74MMP9 NM_004994GAGAACCAATCTCACCGACAGGCAGCTGGCAGAGGAATACCTGTACCGCTATGGTTACACTCGGGTG 75MTA1 NM_004689CCGCCCTCACCTGAAGAGAAACGCGCTCCTTGGCGGACACTGGGGGAGGAGAGGAAGAAGCGCGGCTAACTTATTCC76 MYBL2 NM_002466GCCGAGATCGCCAAGATGTTGCCAGGGAGGACAGACAATGCTGTGAAGAATCACTGGAACTCTACCATCAAAAG77 P14ARF S78535CCCTCGTGCTGATGCTACTGAGGAGCCAGCGTCTAGGGCAGCAGCCGCTTCCTAGAAGACCAGGTCATGATG78 p27 NM_004064CGGTGGACCACGAAGAGTTAACCCGGGACTTGGAGAAGCACTGCAGAGACATGGAAGAGGCGAGCC 79P53 NM_000546CTTTGAACCCTTGCTTGCAATAGGTGTGCGTCAGAAGCACCCAGGACTTCCAMGCTTTGTCCCGGG 80PAI1 NM_000602CCGCAACGTGGTTTTCTCACCCTATGGGGTGGCCTCGGTGTTGGCCATGCTCCAGCTGACAACAGGAGGAGAAACCCAGC81 A PDGFRb NM_002609CCAGCTCTCCTTCCAGCTACAGATCAATGTCCCTGTCCGAGTGCTGGAGCTAAGTGAGAGCCACCC 82PI3KC2A NM_002645ATACCAATCACCGCACAAACCCAGGCTATTTGTTAAGTCCAGTCACAGCGCAAAGAAACATATGCGGAGAAAATGCTAGT83 GTG PPM1D NM_003620GCCATCCGCAAAGGCTTTCTCGCTTGTCACCTTGCCATGTGGAAGAAACTGGCGGAATGGCC 84 PRNM_000926GCATCAGGCTGTCATTATGGTGTCCTTACCTGTGGGAGCTGTAAGGTCTTCTTTAAGAGGGCAATGGAAGGGCAGCACAA85 CTACT PRAME NM_006115TCTCCATATCTGCCTTGCAGAGTCTCCTGCAGCACCTCATCGGGCTGAGCAATCTGACCCACGTGC 86pS2 NM_003225GCCCTCCCAGTGTGCAAATAAGGGCTGCTGTTTCGACGACACCGTTCGTGGGGTCCCCTGGTGCTTCTATCCTAATACCA87 TCGACG RAD51C NM_058216GAACTTCTTGAGCAGGAGCATACCCAGGGCTTCATAATCACCTTCTGTTCAGCACTAGATGATATTCTTGGGGGTGGA88 RB1 NM_000321CGAAGCCCTTACAAGTTTCCTAGTTCACCCTTACGGATTCCTGGAGGGAACATCTATATTTCACCCCTGAAGAGTCC89 RIZ1 NM_012231CCAGACGAGCGATTAGAAGCGGCAGCTTGTGAGGTGAATGATTTGGGGGAAGAGGAGGAGGAGGAAGAGGAGGA90 STK15 NM_003600CATCTTCCAGGAGGACCACTCTCTGTGGCACCCTGGACTACCTGCCCCCTGAAATGATTGAAGGTCGGA 91STMY3 NM_005940CCTGGAGGCTGCAACATACCTCAATCCTGTCCCAGGCCGGATCCTCCTGAAGCCCTTTTCGCAGCACTGCTATCCTCCAA92 AGCCATTGTA SURV NM_001168TGTTTTGATTCCCGGGCTTACCAGGTGAGAAGTGAGGGAGGAAGAAGGCAGTGTCCCTTTTGCTAGAGCTGACAGCTTTG93

TABLE 5D SEQ ID Gene Accession Seq NO: TBP NM_003194GCCCGAAACGCCGAATATAATCCCAAGCGGTTTGCTGCGGTAATCATGAGGATAAGAGAGCCACG 94TGFA NM_003236GGTGTGCCACAGACCTTCCTACTTGGCCTGTAATCACCTGTGCAGCCTTTTGTGGGCCTTCAAAACTCTGTCAAGAACT95 CCGT TIMP1 NM_003254TCCCTGCGGTCCCAGATAGCCTGAATCCTGCCCGGAGTGGAACTGAAGCCTGCACAGTGTCCACCCTGTTCCCAC96 TOP2A NM_001067AATCCAAGGGGGAGAGTGATGACTTCCATATGGACTTTGACTCAGCTGTGGCTCCTCGGGCAAAATCTGTAC97 TOP2B NM_001068TGTGGACATCTTCCCCTCAGACTTCCCTACTGAGCCACCTTCTCTGCCACGAACCGGTCGGGCTAG 98 TPNM_001953CTATATGCAGCCAGAGATGTGACAGCCACCGTGGACAGCCTGCCACTCATCACAGCCTCCATTCTCAGTAAGAAACTCG99 TGG TP538P2 NM_005426GGGCCAAATATTCAGAAGCTTTTATATCAGAGGACCACCATAGCGGCCATGGAGACCATCTCTGTCCCATCATACCCA100 TCC TRAIL NM_003810CTTCACAGTGCTCCTGCAGTCTCTCTGTGTGGCTGTAACTTACGTGTACTTTACCAACGAGCTGAAGCAGATG101 TS NM_001071GCCTCGGTGTGCCTTTCAACATCGCCAGCTACGCCCTGCTCACGTACATGATTGCGCACATCACG 102upa NM_002658GTGGATGTGCCCTGAAGGACAAGCCAGGCGTCTACACGAGAGTCTCACACTTCTTACCCTGGATCCGCAG103 VDR NM_000376GCCCTGGATTTCAGAAAGAGCCAAGTCTGGATCTGGGACCCTTTCCTTCCTTCCCTGGCTTGTAACT 104VEGF NM_003376CTGCTGTCTTGGGTGCATTGGAGCCTTGCCTTGCTGCTCTACCTCCACCATGCCAAGTGGTCCCAGGCTGC105 VEGFB NM_003377TGACGATGGCCTGGAGTGTGTGCCCACTGGGCAGCACCAAGTCCGGATGCAGATCCTCATGATCCGGTACC106 WISP1 NM_003882AGAGGCATCCATGAACTTCACACTTGCGGGCTGCATCAGCACACGCTCCTATCAACCCAAGTACTGTGGAGTTTG107 XIAP NM_001167GCAGTTGGAAGACACAGGAAAGTATCCCCAAATTGCAGATTTATCAACGGCTTTTATCTTGAAAATAGTGCCACGCA108 YB-1 NM_004559AGACTGTGGAGTTTGATGTTGTTGAAGGAGAAAAGGGTGCGGAGGCAGCAAATGTTACAGGTCCTGGTGGTGTTCC109 ZNF217 NM_006526ACCCAGTAGCAAGGAGAAGCCCACTCACTGCTCCGAGTGCGGCAAAGCTTTCAGAACCTACCACCAGCTG110

TABLE 6A SEQ ID Gene Accession Probe Name Seq Length NO: AIB1 NM_006534S1994/AIB1.f3 GCGGCGAGTTTCCGATTTA 19 111 AIB1 NM_006534 S1995/AIB1.r3TGAGTCCACCATCCAGCAAGT 21 112 AIB1 NM_006534 S5055/AIB1.p3ATGGCGGCGGGAGGATCAAAA 21 113 AKT1 NM_005163 S0010/AKT1.f3CGCTTCTATGGCGCTGAGAT 20 114 AKT1 NM_005163 S0012/AKT1.r3TCCCGGTACACCACGTTCTT 20 115 AKT1 NM_005163 S4776/AKT1.p3CAGCCCTGGACTACCTGCACTCGG 24 116 AKT2 NM_001626 S0828/AKT2.f3TCCTGCCACCCTTCAAACC 19 117 AKT2 NM_001626 S0829/AKT2.r3GGCGGTAAATTCATCATCGAA 21 118 AKT2 NM_001626 S4727/AKT2.p3CAGGTCACGTCCGAGGTCGACACA 24 119 APC NM_000038 S0022/APC.f4GGACAGCAGGAATGTGTTTC 20 120 APC NM_000038 S0024/APC.r4ACCCACTCGATTTGTTTCTG 20 121 APC NM_000038 S4888/APC.p4CATTGGCTCCCCGTGACCTGTA 22 122 AREG NM_001657 S0025/AREG.f2TGTGAGTGAAATGCCTTCTAGTAGTGA 27 123 AREG NM_001657 S0027/AREG.r2TTGTGGTTCGTTATCATACTCTTCTGA 27 125 AREG NM_001657 S4889/AREG.p2CCGTCCTCGGGAGCCGACTATGA 23 124 B-actin NM_001101 S0034/B-acti.f2CAGCAGATGTGGATCAGCAAG 21 126 B-actin NM_001101 S0036/8-acti.r2GCATTTGCGGTGGACGAT 18 127 B-actin NM_001101 S4730/B-acti.p2AGGAGTATGACGAGTCCGGCCCC 23 128 B-Catenin NM_001904 S2150/B-Cate.f3GGCTCTTGTGCGTACTGTCCTT 22 129 B-Catenin NM_001904 S2151/B-Cate.r3TCAGATGACGAAGAGCACAGATG 23 130 B-Catenin NM_001904 S5046/B-Cate.p3AGGCTCAGTGATGTCTTCCCTGTCACCAG 29 131 BAD NM_032989 S2011/BAD.f1GGGTCAGGTGCCTCGAGAT 19 132 BAD NM_032989 S2012/BAD.r1CTGCTCACTCGGCTCAAACTC 21 133 BAD NM_032989 S5058/BAD.p1TGGGCCCAGAGCATGTTCCAGATC 24 134 BAG1 NM_004323 S1386/BAG1.f2CGTTGTCAGCACTMGAATACAA 23 135 BAG1 NM_004323 S1387/BAG1.r2GTTCAACCTCTTCCTGTGGACTGT 24 135 BAG1 NM_004323 S4731/BAG1.p2CCCAATTAACATGACCCGGCAACCAT 26 137 BBC3 NM_014417 S1584/BBC3.f2CCTGGAGGGTCCTGTACAAT 20 138 BBC3 NM_014417 S1585/BBC3.r2CTAATTGGGCTCCATCTCG 19 139 BBC3 NM_014417 S4890/BBC3.p2CATCATGGGACTCCTGCCCTTACC 24 140 Bcl2 NM_000633 S0043/Bcl2.f2CAGATGGACCTAGTACCCACTGAGA 25 141 Bcl2 NM_000633 S0045/Bcl2.r2CCTATGATTTAAGGGCATTTTTCC 24 143 Bcl2 NM_000633 S4732/Bcl2.p2TTCCACGCCGAAGGACAGCGAT 22 142 CA9 NM_001216 S1398/CA9.f3ATCCTAGCCCTGGTTTTiGG 20 144 CA9 NM_001216 S1399/CA9.r3CTGCCTTCTCATCTGCACAA 20 145 CA9 NM_001216 S4938/CA9.p3TTTGCTGTCACCAGCGTCGC 20 146 CCNB1 NM_031966 S1720/CCNB1.f2TTCAGGTTGTTGCAGGAGAC 20 147 CCNB1 NM_031966 S1721/CCNB1.r2CATCTTCTTGGGCACACAAT 20 148 CCNB1 NM_031966 S4733/CCNB1.p2TGTCTCCATTATTGATCGGTTCATGCA 27 149 CCND1 NM_001758 S0058/CCND1.f3GCATGTTCGTGGCCTCTAAGA 21 150 CCND1 NM_001758 S0060/CCND1.r3CGGTGTAGATGCACAGCTTCTC 22 151 CCND1 NM_001758 S4986/CCND1.p3AAGGAGACCATCCCCCTGACGGC 23 152 CCNE1 NM_001238 S1446/CCNE1.f1AAAGAAGATGATGACCGGGTTTAC 24 153 CCNE1 NM_001238 S1447/CCNE1.s1GAGCCTCTGGATGGTGCAAT 20 154 CCNE1 NM_001238 S4944/CCNE1.p1CAAACTCAACGTGCAAGCCTCGGA 24 155

TABLE 6B SEQ ID Gene Accession Probe Name Seq Length NO: CCNE2 NM_057749S1458/CCNE2.f2 ATGCTGTGGCTCCTTCCTAACT 22 156 CCNE2 NM_057749S1459/CCNE2.r2 ACCCAAATTGTGATATACAAAAAGGTT 27 157 CCNE2 NM_057749S4945/CCNE2.p2 TACCAAGCAACCTACATGTCAAGAAAGCC 30 158 C CD3z NM_000734S0064/CD3z.f1 AGATGAAGTGGAAGGCGCTT 20 159 CD3z NM_000734 S0066/CD3z.r1TGCCTCTGTAATCGGCAACTG 21 161 CD3z NM_000734 S4988/CD3z.p1CACCGCGGCCATCCTGCA 18 160 CD68 NM_001251 S0067/CD68.f2TGGTTCCCAGCCCTGTGT 18 162 CD68 NM_001251 S0069/CD68.r2CTCCTCCACCCTGGGTTGT 19 164 CD68 NM_001251 S47341CD68.p2CTCCAAGCCCAGATTCAGATTCGAGTCA 28 163 CD9 NM_001769 S0686/CD9.f1GGGCGTGGAACAGTTTATCT 20 165 CD9 NM_001769 S0687/CD9.r1CACGGTGAAGGTTTCGAGT 19 166 CD9 NM_001769 S4792/CD9.p1AGACATCTGCCCCAAGAAGGACGT 24 167 CDH1 NM_004360 S0073/CDH1.f3TGAGTGTCCCCCGGTATCTTC 21 168 CDH1 NM_004360 S0075/CDH1.r3CAGCCGCTTTCAGATTTTCAT 21 169 CDH1 NM_004360 S4990/CDH1.p3TGCCAATCCCGATGAAATTGGAAATTT 27 170 CEGP1 NM_020974 S1494/CEGP1.f2TGACAATCAGCACACCTGCAT 21 171 CEGP1 NM_020974 S1495/CEGP1.r2TGTGACTACAGCCGTGATCCTTA 23 172 CEGP1 NM_020974 S4735/CEGP1.p2CAGGCCCTCTTCCGAGCGGT 20 173 Chk1 NM_001274 S1422/Chk1.f2GATAAATTGGTACAAGGGATCAGCTT 26 174 Chk1 NM_001274 81423/Chk1.r2GGGTGCCAAGTAACTGACTATTCA 24 175 Chk1 NM_001274 S4941/Chk1.p2CCAGCCCACATGTCCTGATCATATGC 26 176 CIAP1 NM_001166 S0764/CIAP.f2TGCCTGTGGTGGGAAGCT 18 177 CIAP1 NM_001166 S0765/CIAP1.r2GGAAAATGCCTCCGGTGTT 19 178 CIAP1 NM_001166 S4802/CIAP1.p2TGACATAGCATCATCCTTTGGTTCCCAGTT 30 179 cIAP2 NM_001165 S0076/cIAP2.f2GGATATTTCCGTGGCTCTTATTCA 24 180 cIAP2 NM_001165 S0078/cIAP2.r2CTTCTCATCAAGGCAGAAAAATCTT 25 182 cIAP2 NM_001165 S4991/cIAP2.p2TCTCCATCAAATCCTGTAAACTCCAGAGCA 30 181 cMet NM_000245 S0082/cMet.f2GACATTTCCAGTCCTGCAGTCA 22 183 cMet NM_000245 S0084/cMet.r2CTCCGATCGCACACATTTGT 20 185 cMet NM_000245 S4993/cMet.p2TGCCTCTCTGCCCCACCCTTTGT 23 184 Contig AK000618 S2633/Contig.f3GGCATCCTGGCCCAAAGT 18 186 27882 Contig AK000618 S2634/Contig.r3GACCCCCTCAGCTGGTAGTTG 21 187 27882 Contig AK000618 S4977/Contig.p3CCCAAATCCAGGCGGCTAGAGGC 23 188 27882 COX2 NM_000963 S0088/C0X2.f1TCTGCAGAGTTGGAAGCACTCTA 23 189 COX2 NM_000963 S0090/C0X2.r1GCCGAGGCTTTTCTACCAGAA 21 191 COX2 NM_000963 S4995/C0X2.p1CAGGATACAGCTCCACAGCATCGATGTC 28 190 CTSL NM_001912 S1303/CTSL.f2GGGAGGCTTATCTCACTGAGTGA 23 192 CTSL NM_001912 S1304/CTSL.r2CCATTGCAGCCTTCATTGC 19 193 CTSL NM_001912 S4899/CTSL.p2TTGAGGCCCAGAGCAGTCTACCAGATTCT 29 194 CTSL2 NM_001333 S4354/CTSL2.f1TGTCTCACTGAGCGAGCAGAA 21 195 CTSL2 NM_001333 S4355/CTSL2.r1ACCATTGCAGCCCTGATTG 19 196 CTSL2 NM_001333 S4356/CTSL2.p1CTTGAGGACGCGAACAGTCCACCA 24 197

TABLE 6C SEQ ID Gene Accession Probe Name Seq Length NO: DAPK1 NM_004938S1768/DAPK1.f3 CGCTGACATCATGAATGTTCCT 22 198 DAPK1 NM_004938S1769/DAPK1.r3 TCTCTTTCAGCAACGATGTGTCTT 24 199 DAPK1 NM_004938S4927/DAPK1.p3 TCATATCCAAACTCGCCTCCAGCCG 25 200 DIABLO NM_019887S0808/DIABLO.f1 CACAATGGCGGCTCTGAAG 19 201 DIABLO NM_019887S0809/DIABLO.r1 ACACAAACACTGTCTGTACCTGAAGA 26 202 DIABLO NM_019887S4813/DIABLO.p1 AAGTTACGCTGCGCGACAGCCAA 23 203 DR5 NM_003842S2551/DR5.f2 CTCTGAGACAGTGCTTCGATGACT 24 204 DR5 NM_003842 S2552/DR5.r2CCATGAGGCCCAACTTCCT 19 205 DR5 NM_003842 S4979/DR5.p2CAGACTTGGTGCCCTTTGACTCC 23 206 EGFR NM_005228 S0103/EGFR.f2TGTCGATGGACTTCCAGAAC 20 207 EGFR NM_005228 S0105/EGFR.r2ATTGGGACAGCTTGGATCA 19 209 EGFR NM_005228 S4999/EGFR.p2CACCTGGGCAGCTGCCAA 18 208 EIF4E NM_001968 S0106/EIF4E.f1GATCTAAGATGGCGACTGTCGAA 23 210 EIF4E NM_001968 S0108/E1F4E.r1TTAGATTCCGTTTTCTCCTCTTCTG 25 211 EIF4E NM_001968 S5000/EIF4E.p1ACCACCCCTACTCCTAATCCCCCGACT 27 212 EMS1 NM_005231 S2663/EMS1.f1GGCAGTGTCACTGAGTCCTTGA 22 213 EMS1 NM_005231 S2664/EMS1.r1TGCACTGTGCGTCCCAAT 18 214 EMS1 NM_005231 S4956/EMS1.p1ATCCTCCCCTGCCCCGCG 18 215 EpCAM NM_002354 S1807/EpCAM.f1GGGCCCTCCAGAACAATGAT 20 216 EpCAM NM_002354 S1808/EpCAM.r1TGCACTGCTTGGCCTTAAAGA 21 217 EpCAM NM_002354 S4984/EpCAM.p1CCGCTCTCATCGCAGTCAGGATCAT 25 218 EPHX1 NM_000120 S1865/EPHX1.f2ACCGTAGGCTCTGCTCTGAA 20 219 EPHX1 NM_000120 S1866/EPHX1.r2TGGTCCAGGTGGAAAACTTC 20 220 EPHX1 NM_000120 S4754/EPHX1.p2AGGCAGCCAGACCCACAGGA 20 221 ErbB3 NM_001982 S0112/ErbB3.f1CGGTTATGTCATGCCAGATACAC 23 222 ErbB3 NM_001982 S0114/ErbB3.r1GAACTGAGACCCACTGAAGAAAGG 24 224 ErbB3 NM_001982 S5002/ErbB3.p1CCTCAAAGGTACTCCCTCCTCCCGG 25 223 EstR1 NM_000125 S0115/EstR1.f1CGTGGTGCCCCTCTATGAC 19 225 EstR1 NM_000125 S0117/EstR1.r1GGCTAGTGGGCGCATGTAG 19 227 EstR1 NM_000125 S4737/EstR1.p1CTGGAGATGCTGGACGCCC 19 226 FBXO5 NM_012177 S2017/FBXO5.r1GGATTGTAGACTGTCACCGAAATTC 25 228 FBXO5 NM_012177 S2018/FBXO5.f1GGCTATTCCTCATTTTCTCTACAAAGTG 28 229 FBXO5 NM_012177 S5061/FBXO5.p1CCTCCAGGAGGCTACCTTCTTCATGTTCAC 30 230 FGF18 NM_003862 S1665/FGF18.f2CGGTAGTCAAGTCCGGATCAA 21 231 FGF18 NM_003862 S1666/FGF18.r2GCTTGCCTTTGCGGTTCA 18 232 FGF18 NM_003862 S49141FGF18.p2CAAGGAGACGGAATTCTACCTGTGC 25 233 FGFR1 NM_023109 S0818/FGFR1.f3CACGGGACATTCACCACATC 20 234 FGFR1 NM_023109 S0819/FGFR1.r3GGGTGCCATCCACTTCACA 19 235 FGFR1 NM_023109 S4816/FGFR1.p3ATAAAAAGACAACCAACGGCCGACTGC 27 236 FHIT NM_002012 S2443/FHIT.f1CCAGTGGAGCGCTTCCAT 18 237 FHIT NM_002012 S2444/FHIT.r1CTCTCTGGGTCGTCTGAAACAA 22 238 FHIT NM_002012 S2445/FHIT.p1TCGGCCACTTCATCAGGACGCAG 23 239 FHIT NM_002012 S4921/FHIT.p1TCGGCCACTTCATCAGGACGCAG 23 239 FRP1 NM_003012 S1804/FRP1.f3TTGGTACCTGTGGGTTAGCA 20 240 FRP1 NM_003012 S1805/FRP1.r3CACATCCAAATGCAAACTGG 20 241

TABLE 6D SEQ ID Gene Accession Probe Name Seq Length NO: FRP1 NM_003012S4983/FRP1.p3 TCCCCAGGGTAGAATTCAATCAGAGC 26 242 G-Catenin NM_002230S2153/G-Cate.f1 TCAGCAGCAAGGGCATCAT 19 243 G-Catenin NM_002230S2154/G-Cate.r1 GGTGGTTTTCTTGAGCGTGTACT 23 244 G-Catenin NM_002230S5044/G-Cate.p1 CGCCCGCAGGCCTCATCCT 19 245 GAPDH NM_002046S0374/GAPDH.f1 ATTCCACCCATGGCAAATTC 20 246 GAPDH NM_002046S0375/GAPDH.r1 GATGGGATTTCCATTGATGACA 22 247 GAPDH NM_002046S4738/GAPDH.p1 CCGTTCTCAGCCTTGACGGTGC 22 248 GATA3 NM_002051S0127/GATA3.f3 CAAAGGAGCTCACTGTGGTGTCT 23 249 GATA3 NM_002051S0129/GATA3.r3 GAGTCAGAATGGCTTATTCACAGATG 26 251 GATA3 NM_002051S5005/GATA3.p3 TGTTCCAACCACTGAATCTGGACC 24 250 GRB7 NM_005310S0130/GRB7.f2 CCATCTGCATCCATCTTGTT 20 252 GRB7 NM_005310 S0132/GRB7.r2GGCCACCAGGGTATTATCTG 20 254 GRB7 NM_005310 S4726/GRB7.p2CTCCCCACCCTTGAGAAGTGCCT 23 253 GRO1 NM_001511 S0133/GRO1.f2CGAAAAGATGCTGAACAGTGACA 23 255 GRO1 NM_001511 S0135/GRO1.r2TCAGGAACAGCCACCAGTGA 20 256 GRO1 NM_001511 S5006/GRO1.p2CTTCCTCCTCCCTTCTGGTCAGTTGGAT 28 257 GSTM1 NM_000561 S2026/GSTM1.f1GGCCCAGCTTGAATTTTTCA 20 258 GSTM1 NM_000561 S2027/GSTM1.f1AAGCTATGAGGAAAAGAAGTACACGAT 27 259 GSTM1 NM_000561 S4739/GSTM1.p1TCAGCCACTGGCTTCTGTCATAATCAGGA 30 260 G GUS NM_000181 S0139/GUS.f1CCCACTCAGTAGCCAAGTCA 20 261 GUS NM_000181 S0141/GUS.r1CACGCAGGTGGTATCAGTCT 20 263 GUS NM_000181 S4740/GUS.p1TCAAGTAAACGGGCTGTTTTCCAAACA 27 262 HER2 NM_004448 S0142/HER2.f3CGGTGTGAGAAGTGCAGCAA 20 264 HER2 NM_004448 S0144/HER2.r3CCTCTCGCAAGTGCTCCAT 19 266 HER2 NM_004448 S4729/HER2.p3CCAGACCATAGCACACTCGGGCAC 24 265 HIF1A NM_001530 S12071HIF1A.f3TGAACATAAAGTCTGCAACATGGA 24 267 HIF1A NM_001530 S1208/HIF1A.r3TGAGGTTGGTTACTGTTGGTATCATATA 28 268 HIF1A NM_001530 S4753/HIF1A.p3TTGCACTGCACAGGCCACATTCAC 24 269 HNF3A NM_004496 S0148/HNF3A.f1TCCAGGATGTTAGGAACTGTGAAG 24 270 HNF3A NM_004496 S0150/HNF3A.r1GCGTGTCTGCGTAGTAGCTGTT 22 271 HNF3A NM_004496 S5008/HNF3A.p1AGTCGCTGGTTTCATGCCCTTCCA 24 272 ID1 NM_002165 S0820/ID1.f1AGAACCGCAAGGTGAGCAA 19 273 ID1 NM_002165 S0821/ID1.r1TCCAACTGAAGGTCCCTGATG 21 274 ID1 NM_002165 S4832/ID1.p1TGGAGATTCTCCAGCACGTCATCGAC 26 275 IGF1 NM_000618 S0154/IGF1.f2TCCGGAGCTGTGATCTAAGGA 21 276 IGF1 NM_000618 S0156/IGF1.r2CGGACAGAGCGAGCTGACTT 20 278 IGF1 NM_000618 S5010/IGF1.p2TGTATTGCGCACCCCTCAAGCCTG 24 277 IGF1R NM_000875 S1249/IGF1R.f3GCATGGTAGCCGAAGATTTCA 21 279 IGF1R NM_000875 S1250/IGF1R.r3TTTCCGGTAATAGTCTGTCTCATAGATATC 30 280 IGF1R NM_000875 S4895/IGF1R.p3CGCGTCATACCAAAATCTCCGATTTTGA 28 281 IGFBP2 NM_000597 S1128/IGFBP2.f1GTGGACAGCACCATGAACA 19 282 IGFBP2 NM_000597 S1129/IGFBP2.s1CCTTCATACCCGACTTGAGG 20 283 IGFBP2 NM_000597 S4837/IGFBP2.p1CTTCCGGCCAGCACTGCCTC 20 284 IL6 NM_000600 S0760/IL6.f3CCTGAACCTTCCAAAGATGG 20 285

TABLE 6E SEQ ID Gene Accession Probe Name Seq Length NO: IL6 NM_000600S0761/IL6.r3 ACCAGGCAAGTCTCCTCATT 20 286 IL6 NM_000600 S4800/IL6.p3CCAGATTGGAAGCATCCATCTTTTTCA 27 287 IRS1 NM_005544 S1943/IRS1.f3CCACAGCTCACCTTCTGTCA 20 288 IRS1 NM_005544 S1944/IRS1.r3CCTCAGTGCCAGTCTCTTCC 20 289 IRS1 NM_005544 S5050/IRS1.p3TCCATCCCAGCTCCAGCCAG 20 290 Ki-67 NM_002417 S0436/Ki-67.f2CGGACTTTGGGTGCGACTT 19 292 Ki-67 NM_002417 S0437/Ki-67.r2TTACAACTCTTCCACTGGGACGAT 24 293 Ki-67 NM_002417 S4741/Ki-67.p2CCACTTGTCGAACCACCGCTCGT 23 291 KLK10 NM_002776 S2624/KLK10.f3GCCCAGAGGCTCCATCGT 18 294 KLK10 NM_002776 S2625/KLK10.r3CAGAGGTTTGAACAGTGCAGACA 23 295 KLK10 NM_002776 S4978/KLK10.p3CCTCTTCCTCCCCAGTCGGCTGA 23 296 KRT14 NM_000526 S1853/KRT14.f1GGCCTGCTGAGATCAAAGAC 20 297 KRT14 NM_000526 S1854/KRT14.r1GTCCACTGTGGCTGTGAGAA 20 298 KRT14 NM_000526 S5037/KRT14.p1TGTTCCTCAGGTCCTCAATGGTCTTG 26 299 KRT17 NM_000422 S0172/KRT17.f2CGAGGATTGGTTCTTCAGCAA 21 300 KRT17 NM_000422 S0174/KRT17.r2ACTCTGCACCAGCTCACTGTTG 22 301 KRT17 NM_000422 S5013/KRT17.p2CACCTCGCGGTTCAGTTCCTCTGT 24 302 KRT18 NM_000224 S1710/KRT18.f2AGAGATCGAGGCTCTCAAGG 20 303 KRT18 NM_000224 S1711/KRT18.r2GGCCTTTTACTTCCTCTTCG 20 304 KRT18 NM_000224 S4762/KRT18.p2TGGTTCTTCTTCATGAAGAGCAGCTCC 27 305 KRT19 NM_002276 S1515/KRT19.f3TGAGCGGCAGAATCAGGAGTA 21 306 KRT19 NM_002276 S1516/KRT19.r3TGCGGTAGGTGGCAATCTC 19 307 KRT19 NM_002276 S4866/KRT19.p3CTCATGGACATCAAGTCGCGGCTG 24 308 KRT5 NM_000424 S0175/KRT5.f3TCAGTGGAGAAGGAGTTGGA 20 309 KRT5 NM_000424 S0177/KRT5.r3TGCCATATCCAGAGGAAACA 20 311 KRT5 NM_000424 S5015/KRT5.p3CCAGTCAACATCTCTGTTGTCACAAGCA 28 310 KRT8 NM_002273 S2588/KRT8.f3GGATGAAGCTTACATGAACAAGGTAGA 27 312 KRT8 NM_002273 S2589/KRT8.r3CATATAGCTGCCTGAGGAAGTTGAT 25 313 KRT8 NM_002273 S4952/KRT8.p3CGTCGGTCAGCCCTTCCAGGC 21 314 LOT1 variant NM_002656 S0692/LOT1v.f2GGAAAGACCACCTGAAAAACCA 22 315 1 LOT1 variant NM_002656 S0693/LOT1v.r2GTACTTCTTCCCACACTCCTCACA 24 316 1 LOT1 variant NM_002656 S4793/LOT1v.p2ACCCACGACCCCAACAAAATGGC 23 317 1 Maspin NM_002639 S0836/Maspin.f2CAGATGGCCACTTTGAGAACATT 23 318 Maspin NM_002639 S0837/Maspin.r2GGCAGCATTAACCACAAGGATT 22 319 Maspin NM_002639 S4835/Maspin.p2AGCTGACAACAGTGTGAACGACCAGACC 28 320 MCM2 NM_004526 S1602/MCM2.f2GACTTTTGCCCGCTACCTTTC 21 321 MCM2 NM_004526 S1603/MCM2.r2GCCACTAACTGCTTCAGTATGAAGAG 26 322 MCM2 NM_004526 S4900/MCM2.p2ACAGCTCATTGTTGTCACGCCGGA 24 323 MCM3 NM_002388 S1524/MCM3.f3GGAGAACAATCCCCTTGAGA 20 324 MCM3 NM_002388 S1525/MCM3.r3ATCTCCTGGATGGTGATGGT 20 325 MCM3 NM_002388 S4870/MCM3.p3TGGCCTTTCTGTCTACAAGGATCACCA 27 326 MCM6 NM_005915 S1704/MCM6.f3TGATGGTCCTATGTGTCACATTCA 24 327 MCM6 NM_005915 S1705/MCM6.r3TGGGACAGGAAACACACCAA 20 328

TABLE 6F SEQ ID Gene Accession Probe Name Seq Length NO: MCM6 NM_005915S4919/MCM6.p3 CAGGTTTCATACCAACACAGGCTTCAGCA 30 329 C MDM2 NM_002392S0830/MDM2.f1 CTACAGGGACGCCATCGAA 19 330 MDM2 NM_002392 S0831/MDM2.r1ATCCAACCAATCACCTGAATGTT 23 331 MDM2 NM_002392 S4834/MDM2.p1CTTACACCAGCATCAAGATCCGG 23 332 MMP9 NM_004994 S0656/MMP9.f1GAGAACCAATCTCACCGACA 20 333 MMP9 NM_004994 S0657/MMP9.r1CACCCGAGTGTAACCATAGC 20 334 MMP9 NM_004994 S4760/MMP9.p1ACAGGTATTCCTCTGCCAGCTGCC 24 335 MTA1 NM_004689 S2369/MTA1.f1CCGCCCTCACCTGAAGAGA 19 336 MTA1 NM_004689 S2370/MTA1.r1GGAATAAGTTAGCCGCGCTTCT 22 337 MTA1 NM_004689 S4855/MTA1.p1CCCAGTGTCCGCCAAGGAGCG 21 338 MYBL2 NM_002466 S3270/MYBL2.f1GCCGAGATCGCCAAGATG 18 339 MYBL2 NM_002466 S3271/MYBL2.r1CTTTTGATGGTAGAGTTCCAGTGATTC 27 340 MYBL2 NM_002466 S4742/MYBL2.p1CAGCATTGTCTGTCCTCCCTGGCA 24 341 P14ARF S78535 S2842/P14ARF.f1CCCTCGTGCTGATGCTACT 19 342 P14ARF S78535 S2843/P14ARF.r1CATCATGACCTGGTCTTCTAGG 22 343 P14ARF S78535 S4971/P14ARF.p1CTGCCCTAGACGCTGGCTCCTC 22 344 p27 NM_004064 S0205/p27.f3CGGTGGACCACGAAGAGTTAA 21 345 p27 NM_004064 S0207/p27.r3GGCTCGCCTCTTCCATGTC 19 347 p27 NM_004064 S4750/p27.p3CCGGGACTTGGAGAAGCACTGCA 23 346 P53 NM_000546 S0208/P53.f2CTTTGAACCCTTGCTTGCAA 20 348 P53 NM_000546 S0210/P53.r2CCCGGGACAAAGCAAATG 18 350 P53 NM_000546 S5065/P53.p2AAGTCCTGGGTGCTTCTGACGCACA 25 349 PAI1 NM_000602 S0211/PAI1.f3CCGCAACGTGGTTTTCTCA 19 351 PAI1 NM_000602 S0213/PAI1.r3TGCTGGGTTTCTCCTCCTGTT 21 353 PAI1 NM_000602 S5066/PAI1.p3CTCGGTGTTGGCCATGCTCCAG 22 352 PDGFRb NM_002609 S1346/PDGFRb.f3CCAGCTCTCCTTCCAGCTAC 20 354 PDGFRb NM_002609 S1347/PDGFRb.r3GGGTGGCTCTCACTTAGCTC 20 355 PDGFRb NM_002609 S4931/PDGFRb.p3ATCAATGTCCCTGTCCGAGTGCTG 24 356 PI3KC2A NM_002645 S2020/PI3KC2.r1CACACTAGCATTTTCTCCGCATA 23 357 PI3KC2A NM_002645 S2021/PI3KC2.f1ATACCAATCACCGCACAAACC 21 358 PI3KC2A NM_002645 S5062/PI3KC2.p1TGCGCTGTGACTGGACTTAACAAATAGCCT 30 359 PPM1D NM_003620 S3159/PPM1D.f1GCCATCCGCAAAGGCTTT 18 360 PPM1D NM_003620 S3160/PPM1D.r1GGCCATTCCGCCAGTTTC 18 361 PPM1D NM_003620 S4856/PPM1D.p1TCGCTTGTCACCTTGCCATGTGG 23 362 PR NM_000926 S1336/PR.f6GCATCAGGCTGTCATTATGG 20 363 PR NM_000926 S1337/PR.r6AGTAGTTGTGCTGCCCTTCC 20 364 PR NM_000926 S4743/PR.p6TGTCCTTACCTGTGGGAGCTGTAAGGTC 28 365 PRAME NM_006115 S1985/PRAME.f3TCTCCATATCTGCCTTGCAGAGT 23 366 PRAME NM_006115 S1986/PRAME.r3GCACGTGGGTCAGATTGCT 19 367 PRAME NM_006115 S4756/PRAME.p3TCCTGCAGCACCTCATCGGGCT 22 368 pS2 NM_003225 S0241/pS2.f2GCCCTCCCAGTGTGCAAAT 19 369 pS2 NM_003225 S0243/pS2.r2CGTCGATGGTATTAGGATAGAAGCA 25 371 pS2 NM_003225 S5026/pS2.p2TGCTGTTTCGACGACACCGTTCG 23 370 RAD51C NM_058216 S2606/RAD51C.f3GAACTTCTTGAGCAGGAGCATACC 24 372

TABLE 6G SEQ ID Gene Accession Probe Name Seq Length NO: RAD51CNM_058216 S2607/RAD51C.r3 TCCACCCCCAAGAATATCATCTAGT 25 373 RAD51CNM_058216 S4764/RAD51C.p3 AGGGCTTCATAATCACCTTCTGTTC 25 374 RB1 NM_000321S2700/RB1.f1 CGAAGCCCTTACAAGTTTCC 20 375 RB1 NM_000321 S2701/RB1.r1GGACTCTTCAGGGGTGAAAT 20 376 RB1 NM_000321 S4765/RB1.p1CCCTTACGGATTCCTGGAGGGAAC 24 377 RIZ1 NM_012231 S1320/RIZ1.f2CCAGACGAGCGATTAGAAGC 20 378 RIZ1 NM_012231 S1321/RIZ1.r2TCCTCCTCTTCCTCCTCCTC 20 379 RIZ1 NM_012231 S4761/RIZ1.p2TGTGAGGTGAATGATTTGGGGGA 23 380 STK15 NM_003600 S0794/STK15.f2CATCTTCCAGGAGGACCACT 20 381 STK15 NM_003600 S0795/STK15.r2TCCGACCTTCAATCATTTCA 20 382 STK15 NM_003600 S4745/STK15.p2CTCTGTGGCACCCTGGACTACCTG 24 383 STMY3 NM_005940 S2067/STMY3.f3CCTGGAGGCTGCAACATACC 20 384 STMY3 NM_005940 S2068/STMY3.r3TACAATGGCTTTGGAGGATAGCA 23 385 STMY3 NM_005940 S4746/STMY3.p3ATCCTCCTGAAGCCCTTTTCGCAGC 25 386 SURV NM_001168 S0259/SURV.f2TGTTTTGATTCCCGGGCTTA 20 387 SURV NM_001168 S0261/SURV.r2CAAAGCTGTCAGCTCTAGCAAAAG 24 389 SURV NM_001168 S4747/SURV.p2TGCCTTCTTCCTCCCTCACTTCTCACCT 28 388 TBP NM_003194 S0262/TBP.f1GCCCGAAACGCCGAATATA 19 390 TBP NM_003194 S0264/TBP.r1CGTGGCTCTCTTATCCTCATGAT 23 392 TBP NM_003194 S4751/TBP.p1TACCGCAGCAAACCGCTT-GGG 21 391 TGFA NM_003236 S0489/TGFA.f2GGTGTGCCACAGACCTTCCT 20 393 TGFA NM_003236 S0490/TGFA.r2ACGGAGTTCTTGACAGAGTTTTGA 24 394 TGFA NM_003236 S4768/TGFA.p2TTGGCCTGTAATCACCTGTGCAGCCTT 27 395 TIMP1 NM_003254 S1695/TIMP1.f3TCCCTGCGGTCCCAGATAG 19 396 TIMP1 NM_003254 S1696/TIMP1.r3GTGGGAACAGGGTGGACACT 20 397 TIMP1 NM_003254 S4918/TIMP1.p3ATCCTGCCCGGAGTGGAACTGAAGC 25 398 TOP2A NM_001067 S0271/TOP2A.f4AATCCAAGGGGGAGAGTGAT 20 399 TOP2A NM_001067 S0273/TOP2A.r4GTACAGATTTTGCCCGAGGA 20 401 TOP2A NM_001067 S4777/TOP2A.p4CATATGGACTTTGACTCAGCTGTGGC 26 400 TOP2B NM_001068 S0274/TOP2B.f2TGTGGACATCTTCCCCTCAGA 21 402 TOP2B NM_001068 S0276/TOP2B.r2CTAGCCCGACCGGTTCGT 18 404 TOP2B NM_001068 S4778/TOP2B.p2TTCCCTACTGAGCCACCTTCTCTG 24 403 TP NM_001953 S0277/TP.f3CTATATGCAGCCAGAGATGTGACA 24 405 TP NM_001953 S0279/TP.r3CCACGAGTTTCTTACTGAGAATGG 24 407 TP NM_001953 S4779/TP.p3ACAGCCTGCCACTCATCACAGCC 23 406 TP53BP2 NM_005426 S1931/TP53BP.f2GGGCCAAATATTCAGAAGC 19 408 TP53BP2 NM_005426 S1932/TP53BP.r2GGATGGGTATGATGGGACAG 20 409 TP53BP2 NM_005426 S5049/TP53BP.p2CCACCATAGCGGCCATGGAG 20 410 TRAIL NM_003810 S2539/TRAIL.f1CTTCACAGTGCTCCTGCAGTCT 22 411 TRAIL NM_003810 S2540/TRAIL.r1CATCTGCTTCAGCTCGTTGGT 21 412 TRAIL NM_003810 S4980/TRAIL.p1AAGTACACGTAAGTTACAGCCACACA 26 413 TS NM_001071 S0280/TS.f1GCCTCGGTGTGCCTTTCA 18 414 TS NM_001071 S0282/TS.r1 CGTGATGTGCGCAATCATG19 416 TS NM_001071 S4780/TS.p1 CATCGCCAGCTACGCCCTGCTC 22 415 upaNM_002658 S0283/upa.f3 GTGGATGTGCCCTGAAGGA 19 417

TABLE 6H SEQ ID Gene Accession Probe Name Seq Length NO: upa NM_002658S0285/upa.r3 CTGCGGATCCAGGGTAAGAA 20 418 upa NM_002658 S4769/upa.p3AAGCCAGGCGTCTACACGAGAGTCTCAC 28 419 VDR NM_000376 S2745/VDR.f2GCCCTGGATTTCAGAAAGAG 20 420 VDR NM_000376 S2746/VDR.r2AGTTACAAGCCAGGGAAGGA 20 421 VDR NM_000376 S4962/VDR.p2CAAGTCTGGATCTGGGACCCTTTCC 25 422 VEGF NM_003376 S0286/VEGF.f1CTGCTGTCTTGGGTGCATTG 20 423 VEGF NM_003376 S0288/VEGF.r1GCAGCCTGGGACCACTTG 18 424 VEGF NM_003376 S4782/VEGF.p1TTGCCTTGCTGCTCTACCTCCACCA 25 425 VEGFB NM_003377 S2724/VEGFB.f1TGACGATGGCCTGGAGTGT 19 426 VEGFB NM_003377 S2725/VEGFB.r1GGTACCGGATCATGAGGATCTG 22 427 VEGFB NM_003377 S4960/VEGFB.p1CTGGGCAGCACCAAGTCCGGA 21 428 WISP1 NM_003882 S1671/WISP1.f1AGAGGCATCCATGAACTTCACA 22 429 WISP1 NM_003882 S1672/WISP1.r1CAAACTCCACAGTACTTGGGTTGA 24 430 WISP1 NM_003882 S4915/WISP1.p1CGGGCTGCATCAGCACACGC 20 431 XIAP NM_001167 S0289/XIAP.f1GCAGTTGGAAGACACAGGAAAGT 23 432 XIAP NM_001167 S0291/XIAP.r1TGCGTGGCACTATTTTCAAGA 21 434 XIAP NM_001167 S4752/XIAP.p1TCCCCAAATTGCAGATTTATCAACGGC 27 433 YB-1 NM_004559 S1194/YB-1.f2AGACTGTGGAGTTTGATGTTGTTGA 25 435 YB-1 NM_004559 51195/YB-1.r2GGAACACCACCAGGACCTGTAA 22 436 YB-1 NM_004559 S4843/YB-1.p2TTGCTGCCTCCGCACCCTTTTCT 23 437 ZNF217 NM_006526 S2739/ZNF217.f3ACCCAGTAGCAAGGAGAAGC 20 438 ZNF217 NM_006526 S2740/ZNF217.r3CAGCTGGTGGTAGGTTCTGA 20 439 ZNF217 NM_006526 S4961/ZNF217.p3CACTCACTGCTCCGAGTGCGG 21 440

What is claimed is:
 1. A method of predicting the likelihood oflong-term survival of an ER-negative breast cancer patient without therecurrence of breast cancer, comprising: (a) isolating RNA from a tissuesample obtained from a breast tumor of the patient; (b) reversetranscribing an RNA transcript of STMY3 to produce a cDNA of STMY3 usingat least one primer comprising a nucleotide sequence selected from thegroup consisting of SEQ ID NO:384, SEQ ID NO:385, and SEQ ID NO:386; (c)amplifying the cDNA of STMY3 to produce an amplicon of the RNAtranscript of STMY3; (d) quantitatively assaying a level of the ampliconof the RNA transcript of STMY3; (e) normalizing said level against alevel of an amplicon of at least one reference RNA transcript in saidtissue sample to provide a normalized STMY3 amplicon level; (f)comparing the normalized STMY3 amplicon level to a normalized STMY3amplicon level in reference breast tumor samples; and (g) predicting thelikelihood of long-term survival without the recurrence of breastcancer, wherein increased normalized STMY3 amplicon level is indicativeof a reduced likelihood of long-term survival without recurrence ofbreast cancer.
 2. The method of claim 1, wherein the cDNA of STMY3 isamplified by polymerase chain reaction.
 3. The method of claim 1,further comprising the step of preparing a report indicating that thepatient has an increased or decreased likelihood of long-term survivalwithout breast cancer recurrence.
 4. The method of claim 1, wherein thelevel of the amplicon of the RNA transcript of STMY3 is represented as athreshold cycle (Ct) value and the normalized STMY3 amplicon level isrepresented as a normalized Ct value.
 5. The method of claim 1, whereinthe reference breast cancer samples comprise at least 40 breast cancersamples.
 6. The method of claim 1, wherein the tissue sample is a fixed,wax-embedded tissue sample.